Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery. [https://machinelearningmastery.com/]
SUMMARY: The project aims to construct a predictive model using various machine learning algorithms and document the end-to-end steps using a template. The Sign Language MNIST dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.
INTRODUCTION: The original MNIST image dataset of handwritten digits is a popular benchmark for image-based machine learning methods. The Sign Language MNIST is presented here and follows the same CSV format with labels and pixel values in single rows to stimulate the community to develop more drop-in replacements. The American Sign Language letter database of hand gestures represent a multi-class problem with 24 classes of letters (excluding J and Z, which require motion).
The dataset format is patterned to match closely with the classic MNIST. Each training and test case represents a label (0-25) as a one-to-one map for each alphabetic letter A-Z (and no cases for 9=J or 25=Z because of gesture motions). The training data (27,455 cases) and test data (7172 instances) are approximately half the size of the standard MNIST but otherwise similar with a header row of the labels, pixel1,pixel2….pixel784 which represent a single 28x28 pixel image with grayscale values between 0-255. The original hand gesture image data represented multiple users repeating the gesture against different backgrounds.
In this Take1 iteration, we will construct a Multilayer Perceptron (MLP) model with five hidden layers to model this dataset.
ANALYSIS: In this Take1 iteration, the MLP model's performance achieved an accuracy score of 94.93% after 50 epochs using the training dataset. The same model processed the test dataset with an accuracy score of 77.93%, which pointed to a high variance error.
CONCLUSION: In this iteration, the TensorFlow MLP model did not appear to be suitable for modeling this dataset. We should consider experimenting another algorithm with this dataset.
Dataset Used: Sign Language MNIST Dataset
Dataset ML Model: Multi-class classification with numerical attributes
Dataset Reference: https://www.kaggle.com/datamunge/sign-language-mnist
One source of potential performance benchmarks: https://www.kaggle.com/datamunge/sign-language-mnist
A deep-learning modeling project generally can be broken down into five major tasks:
# Install the packages to support accessing environment variable and SQL databases
!pip install python-dotenv PyMySQL boto3
Collecting python-dotenv
Downloading https://files.pythonhosted.org/packages/32/2e/e4585559237787966aad0f8fd0fc31df1c4c9eb0e62de458c5b6cde954eb/python_dotenv-0.15.0-py2.py3-none-any.whl
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# Retrieve GPU configuration information from Colab
gpu_info = !nvidia-smi
gpu_info = '\n'.join(gpu_info)
if gpu_info.find('failed') >= 0:
print('Select the Runtime → "Change runtime type" menu to enable a GPU accelerator, ')
print('and then re-execute this cell.')
else:
print(gpu_info)
Wed Nov 11 01:51:04 2020
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 455.32.00 Driver Version: 418.67 CUDA Version: 10.1 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 Tesla V100-SXM2... Off | 00000000:00:04.0 Off | 0 |
| N/A 37C P0 23W / 300W | 0MiB / 16130MiB | 0% Default |
| | | ERR! |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+
# Retrieve memory configuration information from Colab
from psutil import virtual_memory
ram_gb = virtual_memory().total / 1e9
print('Your runtime has {:.1f} gigabytes of available RAM\n'.format(ram_gb))
if ram_gb < 20:
print('To enable a high-RAM runtime, select the Runtime → "Change runtime type"')
print('menu, and then select High-RAM in the Runtime shape dropdown. Then, ')
print('re-execute this cell.')
else:
print('You are using a high-RAM runtime!')
Your runtime has 13.7 gigabytes of available RAM To enable a high-RAM runtime, select the Runtime → "Change runtime type" menu, and then select High-RAM in the Runtime shape dropdown. Then, re-execute this cell.
# Direct Colab to use TensorFlow v2
# %tensorflow_version 2.x
# Retrieve CPU information from the system
ncpu = !nproc
print("The number of available CPUs is:", ncpu[0])
The number of available CPUs is: 2
# Set the random seed number for reproducible results
seedNum = 888
# Load libraries and packages
import random
random.seed(seedNum)
import numpy as np
np.random.seed(seedNum)
import pandas as pd
import matplotlib.pyplot as plt
import os
import sys
import boto3
from datetime import datetime
from dotenv import load_dotenv
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
from sklearn.model_selection import RepeatedKFold
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.compose import ColumnTransformer
# from imblearn.pipeline import Pipeline
# from imblearn.over_sampling import SMOTE
# from imblearn.under_sampling import RandomUnderSampler
import tensorflow as tf
tf.random.set_seed(seedNum)
from tensorflow import keras
# Begin the timer for the script processing
startTimeScript = datetime.now()
# Set up the number of CPU cores available for multi-thread processing
n_jobs = 1
# Set up the flag to stop sending progress emails (setting to True will send status emails!)
notifyStatus = False
# Set Pandas options
pd.set_option("display.max_rows", None)
pd.set_option("display.max_columns", None)
pd.set_option("display.width", 140)
# Set the percentage sizes for splitting the dataset
test_set_size = 0.2
val_set_size = 0.25
# Set the number of folds and iterations for cross validation
n_folds = 5
n_iterations = 2
# Set various default modeling parameters
default_loss = 'categorical_crossentropy'
default_metrics = ['accuracy']
default_optimizer = tf.keras.optimizers.Adam(learning_rate=0.0001)
default_kernel_init = tf.keras.initializers.GlorotUniform(seed=seedNum)
default_epoch = 50
default_batch = 32
# Define the labels to use for graphing the data
train_metric = "accuracy"
validation_metric = "val_accuracy"
train_loss = "loss"
validation_loss = "val_loss"
# Check the number of GPUs accessible through TensorFlow
print('Num GPUs Available:', len(tf.config.list_physical_devices('GPU')))
# Print out the TensorFlow version for confirmation
print('TensorFlow version:', tf.__version__)
Num GPUs Available: 1 TensorFlow version: 2.3.0
# Set up the parent directory location for loading the dotenv files
# useColab = False
# if useColab:
# # Mount Google Drive locally for storing files
# from google.colab import drive
# drive.mount('/content/gdrive')
# gdrivePrefix = '/content/gdrive/My Drive/Colab_Downloads/'
# env_path = '/content/gdrive/My Drive/Colab Notebooks/'
# dotenv_path = env_path + "python_script.env"
# load_dotenv(dotenv_path=dotenv_path)
# Set up the dotenv file for retrieving environment variables
# useLocalPC = False
# if useLocalPC:
# env_path = "/Users/david/PycharmProjects/"
# dotenv_path = env_path + "python_script.env"
# load_dotenv(dotenv_path=dotenv_path)
# Set up the email notification function
def status_notify(msg_text):
access_key = os.environ.get('SNS_ACCESS_KEY')
secret_key = os.environ.get('SNS_SECRET_KEY')
aws_region = os.environ.get('SNS_AWS_REGION')
topic_arn = os.environ.get('SNS_TOPIC_ARN')
if (access_key is None) or (secret_key is None) or (aws_region is None):
sys.exit("Incomplete notification setup info. Script Processing Aborted!!!")
sns = boto3.client('sns', aws_access_key_id=access_key, aws_secret_access_key=secret_key, region_name=aws_region)
response = sns.publish(TopicArn=topic_arn, Message=msg_text)
if response['ResponseMetadata']['HTTPStatusCode'] != 200 :
print('Status notification not OK with HTTP status code:', response['ResponseMetadata']['HTTPStatusCode'])
# Reset the random number generators
def reset_random(x):
random.seed(x)
np.random.seed(x)
tf.random.set_seed(x)
if notifyStatus: status_notify('(TensorFlow Multi-Class) Task 1 - Prepare Environment has begun on ' + datetime.now().strftime('%A %B %d, %Y %I:%M:%S %p'))
dataset_path = 'https://dainesanalytics.com/datasets/kaggle-sign-language-mnist/sign_mnist_train.csv'
Xy_original = pd.read_csv(dataset_path, sep=',')
# Take a peek at the dataframe after import
Xy_original.head()
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| 0 | 3 | 107 | 118 | 127 | 134 | 139 | 143 | 146 | 150 | 153 | 156 | 158 | 160 | 163 | 165 | 159 | 166 | 168 | 170 | 170 | 171 | 171 | 171 | 172 | 171 | 171 | 170 | 170 | 169 | 111 | 121 | 129 | 135 | 141 | 144 | 148 | 151 | 154 | 157 | 160 | 163 | 164 | 170 | 119 | 152 | 171 | 171 | 170 | 171 | 172 | 172 | 172 | 172 | 172 | 171 | 171 | 170 | 113 | 123 | 131 | 137 | 142 | 145 | 150 | 152 | 155 | 158 | 161 | 163 | 164 | 172 | 105 | 142 | 170 | 171 | 171 | 171 | 172 | 172 | 173 | 173 | 172 | 171 | 171 | 171 | 116 | 125 | 133 | 139 | 143 | 146 | 151 | 153 | 156 | 159 | 162 | 163 | 167 | 167 | 95 | 144 | 171 | 172 | 172 | 172 | 172 | 172 | 173 | 173 | 173 | 172 | 172 | 171 | 117 | 126 | 134 | 140 | 145 | 149 | 153 | 156 | 158 | 161 | 163 | 164 | 175 | 156 | 87 | 154 | 172 | 173 | 173 | 173 | 173 | 173 | 174 | 174 | 174 | 173 | 172 | 172 | 119 | 128 | 136 | 142 | 146 | 150 | 153 | 156 | 159 | 163 | 165 | 164 | 184 | 148 | 89 | 164 | 172 | 174 | 174 | 174 | 174 | 175 | 175 | 174 | 175 | 174 | 173 | 173 | 122 | 130 | 138 | 143 | 147 | 150 | 154 | 158 | 162 | 165 | 166 | 172 | 181 | 128 | 94 | 170 | 173 | 175 | 174 | 175 | 176 | 177 | 177 | 177 | 177 | 175 | 175 | 174 | 122 | 132 | 139 | 145 | 149 | 152 | 156 | 160 | 163 | 165 | 166 | 181 | 172 | 103 | 113 | 175 | 176 | 178 | 178 | 179 | 179 | 179 | 179 | 178 | 179 | 177 | 175 | 174 | 125 | 134 | 141 | 147 | 150 | 153 | 157 | 161 | 164 | 167 | 168 | 184 | 179 | 116 | 126 | 165 | 176 | 179 | 180 | 180 | 181 | 180 | 180 | 180 | 179 | 178 | 177 | 176 | 128 | 135 | 142 | 148 | 152 | 154 | 158 | 162 | 165 | 168 | 170 | 187 | 180 | 156 | 161 | 124 | 143 | 179 | 178 | 178 | 181 | 182 | 181 | 180 | 181 | 180 | 179 | 179 | 129 | 136 | 144 | 150 | 153 | 155 | 159 | 163 | 166 | 169 | 172 | 187 | 184 | 153 | 102 | 117 | 110 | 175 | 169 | 154 | 182 | 183 | 183 | 182 | 182 | 181 | 181 | 179 | 131 | 138 | 145 | 150 | 155 | 157 | 161 | 165 | 168 | 174 | 190 | 189 | 175 | 146 | 94 | 97 | 113 | 151 | 158 | 129 | 184 | 184 | 184 | 184 | 183 | 183 | 182 | 180 | 131 | 139 | 146 | 151 | 155 | 159 | 163 | 167 | 175 | 182 | 179 | 171 | 159 | 114 | 102 | 89 | 121 | 136 | 136 | 96 | 172 | 186 | 186 | 185 | 185 | 184 | 182 | 181 | 131 | 140 | 147 | 154 | 157 | 160 | 164 | 179 | 186 | 191 | 187 | 180 | 157 | 100 | 88 | 84 | 108 | 111 | 126 | 90 | 120 | 186 | 187 | 187 | 186 | 185 | 184 | 182 | 133 | 141 | 149 | 155 | 158 | 160 | 174 | 201 | 189 | 165 | 151 | 143 | 146 | 120 | 87 | 78 | 87 | 76 | 108 | 98 | 96 | 181 | 188 | 187 | 186 | 186 | 185 | 183 | 133 | 141 | 150 | 156 | 160 | 161 | 179 | 197 | 174 | 135 | 99 | 72 | 95 | 134 | 97 | 72 | 74 | 68 | 116 | 105 | 108 | 187 | 189 | 187 | 187 | 186 | 186 | 185 | 134 | 143 | 151 | 156 | 161 | 163 | 179 | 194 | 156 | 110 | 74 | 42 | 52 | 139 | 94 | 67 | 75 | 75 | 118 | 106 | 129 | 189 | 191 | 190 | 188 | 188 | 187 | 186 | 135 | 144 | 152 | 158 | 163 | 163 | 177 | 193 | 161 | 122 | 84 | 43 | 71 | 134 | 81 | 57 | 71 | 88 | 112 | 98 | 157 | 193 | 193 | 192 | 190 | 190 | 189 | 188 | 136 | 144 | 152 | 158 | 162 | 163 | 176 | 192 | 164 | 128 | 98 | 62 | 60 | 100 | 71 | 76 | 96 | 101 | 105 | 95 | 174 | 195 | 194 | 194 | 194 | 193 | 191 | 190 | 137 | 145 | 152 | 159 | 164 | 165 | 178 | 191 | 164 | 135 | 113 | 82 | 59 | 87 | 98 | 111 | 120 | 108 | 97 | 108 | 190 | 196 | 195 | 195 | 194 | 193 | 193 | 192 | 139 | 146 | 154 | 160 | 164 | 165 | 175 | 186 | 163 | 139 | 112 | 85 | 67 | 102 | 126 | 133 | 126 | 105 | 104 | 176 | 197 | 198 | 197 | 196 | 195 | 195 | 194 | 193 | 138 | 147 | 155 | 161 | 165 | 167 | 172 | 186 | 163 | 137 | 107 | 87 | 76 | 106 | 122 | 125 | 117 | 96 | 156 | 199 | 199 | 200 | 198 | 196 | 196 | 195 | 195 | 194 | 139 | 148 | 156 | 163 | 166 | 168 | 172 | 180 | 158 | 131 | 108 | 99 | 86 | 108 | 118 | 116 | 103 | 107 | 191 | 202 | 201 | 200 | 200 | 200 | 199 | 197 | 198 | 196 | 140 | 149 | 157 | 164 | 168 | 167 | 177 | 178 | 155 | 131 | 118 | 105 | 87 | 100 | 106 | 100 | 96 | 164 | 202 | 202 | 202 | 202 | 202 | 201 | 200 | 199 | 199 | 198 | 140 | 150 | 157 | 165 | 167 | 170 | 181 | 175 | 152 | 130 | 115 | 98 | 82 | 85 | 90 | 99 | 165 | 202 | 203 | 204 | 203 | 203 | 202 | 202 | 201 | 201 | 200 | 200 | 142 | 150 | 159 | 165 | 170 | 191 | 173 | 157 | 144 | 119 | 97 | 84 | 79 | 79 | 91 | 172 | 202 | 203 | 203 | 205 | 204 | 204 | 204 | 203 | 202 | 202 | 201 | 200 | 142 | 151 | 160 | 165 | 188 | 190 | 187 | 150 | 119 | 109 | 85 | 79 | 79 | 78 | 137 | 203 | 205 | 206 | 206 | 207 | 207 | 206 | 206 | 204 | 205 | 204 | 203 | 202 | 142 | 151 | 160 | 172 | 196 | 188 | 188 | 190 | 135 | 96 | 86 | 77 | 77 | 79 | 176 | 205 | 207 | 207 | 207 | 207 | 207 | 207 | 206 | 206 | 206 | 204 | 203 | 202 |
| 1 | 6 | 155 | 157 | 156 | 156 | 156 | 157 | 156 | 158 | 158 | 157 | 158 | 156 | 154 | 154 | 153 | 152 | 151 | 149 | 149 | 148 | 147 | 146 | 144 | 142 | 143 | 138 | 92 | 108 | 158 | 159 | 159 | 159 | 160 | 160 | 160 | 160 | 160 | 160 | 160 | 159 | 158 | 157 | 155 | 154 | 153 | 152 | 151 | 150 | 149 | 149 | 147 | 147 | 146 | 142 | 116 | 143 | 161 | 161 | 161 | 161 | 162 | 161 | 162 | 162 | 162 | 162 | 161 | 161 | 161 | 160 | 159 | 158 | 156 | 155 | 154 | 153 | 152 | 152 | 151 | 150 | 147 | 147 | 125 | 140 | 165 | 164 | 164 | 165 | 165 | 165 | 165 | 165 | 164 | 164 | 164 | 165 | 163 | 163 | 162 | 161 | 159 | 159 | 158 | 156 | 156 | 155 | 152 | 153 | 154 | 151 | 124 | 126 | 166 | 167 | 166 | 167 | 167 | 166 | 167 | 167 | 167 | 167 | 166 | 167 | 165 | 165 | 164 | 163 | 162 | 162 | 161 | 160 | 156 | 151 | 154 | 176 | 145 | 122 | 144 | 100 | 168 | 169 | 168 | 169 | 169 | 168 | 169 | 170 | 170 | 170 | 169 | 168 | 167 | 166 | 167 | 165 | 162 | 159 | 159 | 156 | 151 | 165 | 171 | 146 | 94 | 130 | 159 | 111 | 171 | 171 | 170 | 171 | 171 | 171 | 172 | 171 | 171 | 171 | 172 | 169 | 169 | 170 | 166 | 165 | 160 | 157 | 170 | 177 | 171 | 153 | 124 | 96 | 125 | 157 | 155 | 146 | 172 | 172 | 172 | 173 | 173 | 173 | 173 | 173 | 173 | 173 | 174 | 174 | 171 | 167 | 169 | 175 | 171 | 164 | 165 | 157 | 129 | 112 | 121 | 148 | 164 | 158 | 155 | 152 | 175 | 174 | 174 | 174 | 175 | 174 | 174 | 174 | 174 | 177 | 178 | 174 | 170 | 178 | 182 | 171 | 154 | 127 | 120 | 126 | 138 | 159 | 168 | 165 | 162 | 161 | 158 | 157 | 176 | 176 | 176 | 176 | 177 | 176 | 176 | 177 | 177 | 175 | 169 | 170 | 178 | 169 | 158 | 163 | 139 | 119 | 155 | 171 | 172 | 168 | 165 | 165 | 163 | 162 | 160 | 158 | 177 | 177 | 177 | 178 | 177 | 178 | 178 | 177 | 171 | 159 | 167 | 173 | 157 | 142 | 163 | 152 | 133 | 167 | 177 | 171 | 170 | 170 | 168 | 167 | 166 | 164 | 161 | 159 | 178 | 178 | 179 | 179 | 180 | 178 | 164 | 141 | 137 | 145 | 150 | 141 | 134 | 150 | 154 | 151 | 177 | 181 | 175 | 172 | 173 | 172 | 170 | 167 | 167 | 165 | 163 | 161 | 180 | 179 | 180 | 182 | 180 | 180 | 170 | 156 | 151 | 148 | 151 | 154 | 153 | 153 | 148 | 153 | 147 | 140 | 171 | 176 | 173 | 173 | 171 | 170 | 168 | 167 | 165 | 163 | 182 | 181 | 181 | 182 | 179 | 164 | 149 | 156 | 159 | 153 | 153 | 166 | 173 | 169 | 169 | 163 | 151 | 105 | 141 | 182 | 174 | 175 | 173 | 171 | 171 | 168 | 166 | 165 | 183 | 183 | 183 | 182 | 171 | 159 | 130 | 125 | 135 | 134 | 130 | 137 | 146 | 147 | 164 | 165 | 147 | 103 | 151 | 182 | 176 | 176 | 175 | 173 | 172 | 170 | 167 | 166 | 184 | 185 | 185 | 176 | 174 | 163 | 147 | 145 | 142 | 141 | 141 | 133 | 125 | 125 | 135 | 135 | 112 | 105 | 181 | 179 | 176 | 177 | 175 | 174 | 173 | 171 | 169 | 166 | 185 | 186 | 182 | 170 | 161 | 132 | 125 | 134 | 130 | 144 | 157 | 159 | 152 | 141 | 132 | 121 | 72 | 142 | 176 | 175 | 182 | 177 | 177 | 177 | 175 | 172 | 169 | 168 | 186 | 186 | 173 | 168 | 159 | 128 | 115 | 119 | 109 | 111 | 140 | 154 | 156 | 153 | 140 | 118 | 64 | 67 | 121 | 148 | 160 | 181 | 177 | 177 | 176 | 173 | 169 | 177 | 189 | 178 | 165 | 173 | 163 | 138 | 128 | 128 | 124 | 117 | 111 | 106 | 118 | 139 | 133 | 93 | 40 | 53 | 109 | 146 | 119 | 174 | 179 | 176 | 175 | 174 | 177 | 168 | 186 | 168 | 169 | 169 | 155 | 151 | 145 | 139 | 135 | 130 | 121 | 112 | 104 | 95 | 101 | 61 | 39 | 71 | 99 | 114 | 103 | 177 | 180 | 178 | 178 | 176 | 130 | 171 | 168 | 166 | 163 | 163 | 158 | 149 | 139 | 137 | 138 | 132 | 122 | 116 | 107 | 72 | 66 | 57 | 55 | 63 | 77 | 95 | 155 | 189 | 186 | 177 | 158 | 115 | 86 | 159 | 162 | 162 | 159 | 168 | 170 | 135 | 106 | 97 | 82 | 83 | 103 | 111 | 87 | 68 | 65 | 54 | 89 | 144 | 155 | 173 | 182 | 157 | 165 | 147 | 109 | 134 | 153 | 145 | 165 | 156 | 162 | 169 | 151 | 101 | 73 | 55 | 54 | 65 | 94 | 98 | 71 | 64 | 53 | 114 | 189 | 183 | 177 | 159 | 147 | 142 | 149 | 126 | 160 | 172 | 130 | 92 | 164 | 169 | 171 | 155 | 114 | 82 | 69 | 52 | 62 | 75 | 91 | 82 | 69 | 62 | 139 | 191 | 154 | 120 | 118 | 137 | 147 | 163 | 145 | 159 | 180 | 126 | 87 | 88 | 162 | 170 | 162 | 135 | 98 | 79 | 66 | 58 | 67 | 68 | 66 | 64 | 79 | 146 | 189 | 148 | 132 | 136 | 133 | 144 | 154 | 184 | 139 | 130 | 121 | 93 | 102 | 100 | 162 | 159 | 145 | 112 | 85 | 75 | 68 | 65 | 64 | 62 | 79 | 123 | 192 | 198 | 183 | 126 | 81 | 123 | 137 | 129 | 154 | 217 | 133 | 87 | 87 | 91 | 101 | 94 | 153 | 139 | 115 | 89 | 77 | 72 | 65 | 77 | 106 | 137 | 174 | 185 | 146 | 121 | 111 | 112 | 100 | 78 | 120 | 157 | 168 | 107 | 99 | 121 | 133 | 97 | 95 | 120 | 135 | 116 | 95 | 79 | 69 | 86 | 139 | 173 | 200 | 185 | 175 | 198 | 124 | 118 | 94 | 140 | 133 | 84 | 69 | 149 | 128 | 87 | 94 | 163 | 175 | 103 | 135 | 149 |
| 2 | 2 | 187 | 188 | 188 | 187 | 187 | 186 | 187 | 188 | 187 | 186 | 185 | 185 | 185 | 184 | 184 | 184 | 181 | 181 | 179 | 179 | 179 | 178 | 178 | 109 | 52 | 66 | 77 | 83 | 188 | 189 | 189 | 188 | 188 | 189 | 188 | 188 | 188 | 188 | 187 | 185 | 185 | 187 | 182 | 177 | 182 | 182 | 182 | 180 | 180 | 179 | 180 | 135 | 67 | 73 | 73 | 71 | 190 | 190 | 190 | 191 | 190 | 190 | 189 | 189 | 189 | 188 | 189 | 188 | 192 | 184 | 144 | 103 | 144 | 142 | 138 | 186 | 182 | 180 | 182 | 163 | 81 | 74 | 68 | 61 | 191 | 193 | 192 | 192 | 192 | 191 | 191 | 191 | 191 | 195 | 200 | 189 | 165 | 127 | 98 | 71 | 85 | 94 | 86 | 165 | 184 | 182 | 182 | 175 | 87 | 61 | 51 | 56 | 192 | 193 | 194 | 193 | 193 | 193 | 194 | 191 | 202 | 208 | 193 | 130 | 95 | 78 | 69 | 62 | 69 | 72 | 69 | 83 | 179 | 184 | 183 | 184 | 116 | 43 | 38 | 56 | 193 | 193 | 194 | 195 | 195 | 195 | 195 | 196 | 209 | 193 | 144 | 83 | 58 | 61 | 62 | 52 | 60 | 65 | 66 | 70 | 176 | 187 | 184 | 185 | 158 | 45 | 35 | 45 | 194 | 194 | 195 | 196 | 196 | 197 | 195 | 208 | 212 | 133 | 86 | 67 | 52 | 52 | 60 | 61 | 63 | 73 | 91 | 150 | 187 | 187 | 185 | 183 | 175 | 112 | 83 | 55 | 195 | 196 | 195 | 198 | 198 | 198 | 194 | 219 | 193 | 106 | 70 | 68 | 64 | 60 | 63 | 71 | 150 | 174 | 187 | 191 | 187 | 186 | 185 | 185 | 179 | 122 | 145 | 140 | 195 | 197 | 198 | 199 | 200 | 198 | 202 | 224 | 172 | 103 | 69 | 68 | 66 | 60 | 94 | 163 | 195 | 193 | 190 | 190 | 189 | 189 | 187 | 189 | 170 | 89 | 169 | 171 | 198 | 200 | 200 | 200 | 200 | 199 | 215 | 210 | 146 | 84 | 60 | 60 | 61 | 66 | 168 | 201 | 193 | 192 | 191 | 190 | 189 | 189 | 188 | 189 | 169 | 110 | 182 | 143 | 199 | 200 | 201 | 201 | 200 | 203 | 224 | 197 | 142 | 93 | 74 | 68 | 59 | 70 | 177 | 199 | 195 | 194 | 193 | 192 | 191 | 189 | 189 | 189 | 182 | 152 | 130 | 102 | 201 | 201 | 202 | 202 | 200 | 215 | 229 | 186 | 131 | 93 | 82 | 80 | 68 | 81 | 195 | 196 | 195 | 195 | 195 | 193 | 192 | 192 | 191 | 189 | 189 | 183 | 131 | 130 | 201 | 202 | 202 | 203 | 202 | 230 | 222 | 168 | 118 | 87 | 80 | 78 | 75 | 110 | 202 | 197 | 197 | 196 | 195 | 194 | 194 | 193 | 192 | 191 | 190 | 185 | 169 | 161 | 202 | 202 | 203 | 202 | 207 | 237 | 210 | 158 | 108 | 81 | 76 | 76 | 75 | 116 | 205 | 197 | 198 | 198 | 196 | 195 | 195 | 194 | 193 | 191 | 189 | 186 | 174 | 182 | 202 | 203 | 205 | 200 | 218 | 238 | 202 | 151 | 105 | 82 | 79 | 80 | 75 | 129 | 206 | 198 | 199 | 198 | 197 | 196 | 196 | 194 | 193 | 192 | 190 | 189 | 184 | 166 | 204 | 204 | 207 | 202 | 234 | 228 | 186 | 141 | 97 | 80 | 80 | 79 | 68 | 136 | 209 | 198 | 199 | 199 | 198 | 197 | 198 | 197 | 196 | 192 | 192 | 191 | 190 | 158 | 206 | 206 | 202 | 215 | 240 | 217 | 174 | 130 | 91 | 78 | 74 | 71 | 70 | 83 | 192 | 203 | 200 | 200 | 201 | 200 | 191 | 177 | 164 | 151 | 144 | 164 | 190 | 187 | 206 | 207 | 199 | 229 | 234 | 204 | 160 | 119 | 89 | 75 | 66 | 69 | 73 | 69 | 121 | 212 | 204 | 203 | 189 | 164 | 151 | 151 | 146 | 138 | 119 | 91 | 182 | 191 | 208 | 205 | 206 | 238 | 225 | 190 | 149 | 113 | 89 | 70 | 62 | 68 | 73 | 75 | 74 | 156 | 179 | 161 | 147 | 147 | 155 | 151 | 133 | 112 | 117 | 123 | 184 | 189 | 207 | 198 | 224 | 237 | 218 | 181 | 139 | 107 | 85 | 63 | 62 | 75 | 80 | 82 | 83 | 80 | 111 | 136 | 157 | 158 | 140 | 109 | 103 | 154 | 190 | 192 | 190 | 190 | 199 | 209 | 238 | 232 | 209 | 173 | 137 | 100 | 80 | 58 | 73 | 90 | 97 | 102 | 99 | 100 | 117 | 139 | 145 | 122 | 95 | 94 | 175 | 199 | 195 | 193 | 193 | 190 | 200 | 233 | 237 | 224 | 201 | 167 | 129 | 96 | 80 | 61 | 86 | 98 | 106 | 112 | 107 | 107 | 114 | 115 | 103 | 115 | 153 | 187 | 199 | 195 | 194 | 192 | 190 | 190 | 228 | 236 | 226 | 214 | 194 | 160 | 120 | 92 | 78 | 70 | 95 | 104 | 107 | 111 | 103 | 94 | 83 | 100 | 158 | 197 | 202 | 200 | 197 | 195 | 194 | 192 | 190 | 189 | 227 | 228 | 220 | 206 | 183 | 148 | 106 | 90 | 78 | 83 | 103 | 110 | 106 | 95 | 85 | 77 | 121 | 189 | 205 | 202 | 200 | 201 | 198 | 196 | 195 | 192 | 190 | 190 | 219 | 216 | 204 | 182 | 160 | 127 | 96 | 87 | 77 | 89 | 100 | 104 | 94 | 72 | 103 | 174 | 204 | 205 | 203 | 201 | 200 | 201 | 199 | 197 | 195 | 193 | 192 | 191 | 212 | 198 | 175 | 158 | 136 | 103 | 85 | 76 | 81 | 89 | 92 | 88 | 69 | 108 | 195 | 208 | 204 | 203 | 203 | 201 | 200 | 200 | 199 | 196 | 195 | 195 | 193 | 192 | 202 | 179 | 152 | 140 | 118 | 85 | 75 | 78 | 82 | 77 | 75 | 87 | 142 | 203 | 208 | 205 | 203 | 204 | 203 | 201 | 200 | 200 | 199 | 198 | 196 | 195 | 194 | 193 | 198 | 166 | 132 | 114 | 89 | 74 | 79 | 77 | 74 | 78 | 132 | 188 | 210 | 209 | 206 | 205 | 204 | 203 | 202 | 201 | 200 | 199 | 198 | 199 | 198 | 195 | 194 | 195 |
| 3 | 2 | 211 | 211 | 212 | 212 | 211 | 210 | 211 | 210 | 210 | 211 | 209 | 207 | 208 | 207 | 206 | 203 | 202 | 201 | 200 | 198 | 197 | 195 | 192 | 197 | 171 | 51 | 52 | 54 | 212 | 213 | 215 | 215 | 212 | 212 | 213 | 212 | 212 | 211 | 211 | 209 | 208 | 209 | 206 | 204 | 203 | 202 | 201 | 200 | 199 | 197 | 193 | 204 | 149 | 44 | 49 | 46 | 215 | 217 | 218 | 217 | 216 | 216 | 217 | 214 | 213 | 212 | 212 | 211 | 210 | 211 | 208 | 206 | 206 | 204 | 204 | 202 | 202 | 200 | 194 | 205 | 120 | 52 | 41 | 45 | 218 | 218 | 218 | 218 | 218 | 218 | 218 | 216 | 216 | 215 | 213 | 212 | 213 | 211 | 209 | 208 | 209 | 208 | 208 | 208 | 207 | 202 | 198 | 201 | 90 | 46 | 44 | 34 | 219 | 218 | 220 | 221 | 220 | 220 | 218 | 217 | 219 | 217 | 215 | 214 | 214 | 211 | 201 | 187 | 186 | 182 | 179 | 183 | 192 | 204 | 204 | 196 | 63 | 56 | 49 | 36 | 221 | 221 | 221 | 222 | 222 | 222 | 220 | 220 | 219 | 218 | 217 | 214 | 213 | 212 | 198 | 193 | 186 | 178 | 164 | 150 | 144 | 153 | 192 | 203 | 79 | 51 | 35 | 33 | 224 | 222 | 223 | 223 | 223 | 223 | 220 | 221 | 220 | 219 | 218 | 211 | 208 | 214 | 197 | 176 | 200 | 165 | 145 | 179 | 155 | 141 | 136 | 167 | 93 | 48 | 33 | 32 | 225 | 225 | 225 | 224 | 223 | 223 | 223 | 222 | 221 | 223 | 217 | 210 | 210 | 214 | 155 | 82 | 119 | 104 | 81 | 148 | 158 | 146 | 100 | 89 | 69 | 41 | 38 | 29 | 225 | 226 | 226 | 225 | 226 | 225 | 224 | 223 | 223 | 215 | 216 | 212 | 175 | 151 | 110 | 71 | 63 | 61 | 61 | 84 | 110 | 102 | 80 | 78 | 74 | 50 | 48 | 42 | 226 | 226 | 226 | 226 | 226 | 226 | 226 | 224 | 219 | 217 | 228 | 197 | 126 | 81 | 71 | 70 | 62 | 51 | 60 | 59 | 68 | 75 | 68 | 69 | 91 | 141 | 135 | 60 | 227 | 228 | 228 | 227 | 227 | 227 | 228 | 223 | 222 | 235 | 223 | 174 | 127 | 96 | 60 | 58 | 65 | 85 | 137 | 136 | 185 | 189 | 154 | 155 | 161 | 197 | 182 | 104 | 228 | 229 | 229 | 230 | 228 | 229 | 222 | 224 | 228 | 219 | 183 | 138 | 109 | 93 | 69 | 62 | 60 | 171 | 233 | 222 | 217 | 216 | 219 | 213 | 170 | 175 | 120 | 121 | 230 | 231 | 231 | 230 | 232 | 225 | 223 | 242 | 225 | 205 | 167 | 102 | 82 | 81 | 73 | 62 | 143 | 221 | 215 | 216 | 215 | 211 | 211 | 210 | 185 | 166 | 108 | 168 | 231 | 232 | 232 | 234 | 227 | 225 | 248 | 241 | 213 | 201 | 164 | 108 | 68 | 73 | 72 | 177 | 229 | 219 | 218 | 219 | 218 | 216 | 215 | 214 | 198 | 181 | 156 | 152 | 233 | 233 | 235 | 230 | 226 | 246 | 243 | 224 | 208 | 181 | 132 | 101 | 82 | 58 | 131 | 237 | 219 | 222 | 222 | 220 | 217 | 216 | 216 | 215 | 208 | 182 | 184 | 167 | 235 | 236 | 234 | 226 | 250 | 250 | 230 | 218 | 201 | 162 | 124 | 93 | 62 | 82 | 214 | 228 | 223 | 224 | 225 | 223 | 219 | 217 | 216 | 215 | 214 | 196 | 174 | 181 | 235 | 237 | 233 | 248 | 255 | 244 | 222 | 209 | 182 | 148 | 116 | 82 | 50 | 126 | 246 | 222 | 226 | 226 | 226 | 225 | 219 | 219 | 222 | 213 | 213 | 217 | 180 | 122 | 237 | 234 | 245 | 255 | 252 | 232 | 212 | 198 | 171 | 129 | 98 | 66 | 66 | 68 | 202 | 234 | 224 | 226 | 226 | 223 | 226 | 216 | 182 | 162 | 151 | 187 | 218 | 84 | 233 | 244 | 255 | 247 | 238 | 223 | 202 | 186 | 155 | 110 | 84 | 61 | 73 | 64 | 85 | 225 | 227 | 223 | 228 | 237 | 206 | 145 | 144 | 164 | 111 | 139 | 227 | 163 | 241 | 255 | 252 | 237 | 225 | 209 | 190 | 173 | 135 | 102 | 77 | 76 | 88 | 92 | 67 | 152 | 245 | 231 | 206 | 170 | 130 | 138 | 160 | 121 | 128 | 210 | 211 | 215 | 228 | 255 | 249 | 225 | 216 | 198 | 185 | 162 | 123 | 100 | 82 | 92 | 100 | 98 | 94 | 81 | 147 | 144 | 106 | 94 | 104 | 144 | 114 | 106 | 220 | 215 | 213 | 210 | 90 | 146 | 207 | 193 | 183 | 193 | 175 | 148 | 120 | 94 | 83 | 99 | 102 | 99 | 97 | 89 | 78 | 87 | 98 | 122 | 111 | 117 | 61 | 177 | 226 | 215 | 215 | 211 | 74 | 63 | 80 | 119 | 134 | 134 | 136 | 117 | 90 | 77 | 82 | 97 | 97 | 97 | 93 | 89 | 86 | 106 | 109 | 117 | 102 | 63 | 91 | 224 | 218 | 218 | 215 | 212 | 71 | 72 | 65 | 65 | 65 | 71 | 75 | 57 | 54 | 66 | 70 | 83 | 88 | 88 | 84 | 89 | 90 | 95 | 87 | 77 | 89 | 144 | 210 | 224 | 219 | 219 | 217 | 214 | 67 | 66 | 63 | 65 | 58 | 29 | 34 | 43 | 46 | 53 | 52 | 66 | 79 | 74 | 67 | 64 | 57 | 70 | 115 | 167 | 216 | 236 | 228 | 223 | 221 | 220 | 219 | 217 | 65 | 62 | 58 | 55 | 63 | 44 | 11 | 22 | 24 | 28 | 34 | 49 | 46 | 62 | 95 | 109 | 141 | 202 | 237 | 240 | 231 | 226 | 226 | 224 | 222 | 220 | 219 | 217 | 63 | 56 | 52 | 52 | 59 | 56 | 17 | 16 | 20 | 17 | 17 | 14 | 120 | 217 | 237 | 244 | 247 | 242 | 233 | 231 | 230 | 229 | 227 | 225 | 223 | 221 | 220 | 216 | 58 | 51 | 49 | 50 | 57 | 60 | 17 | 15 | 18 | 17 | 19 | 1 | 159 | 255 | 237 | 239 | 237 | 236 | 235 | 234 | 233 | 231 | 230 | 226 | 225 | 222 | 229 | 163 |
| 4 | 13 | 164 | 167 | 170 | 172 | 176 | 179 | 180 | 184 | 185 | 186 | 188 | 189 | 189 | 190 | 191 | 189 | 190 | 190 | 187 | 190 | 192 | 193 | 191 | 191 | 192 | 192 | 194 | 194 | 166 | 169 | 172 | 174 | 177 | 180 | 182 | 185 | 186 | 187 | 190 | 191 | 190 | 191 | 192 | 192 | 194 | 182 | 152 | 146 | 189 | 195 | 194 | 193 | 193 | 194 | 195 | 195 | 167 | 170 | 173 | 176 | 178 | 181 | 183 | 186 | 187 | 188 | 191 | 193 | 192 | 191 | 188 | 193 | 206 | 182 | 136 | 92 | 172 | 196 | 196 | 197 | 195 | 197 | 197 | 196 | 170 | 172 | 175 | 177 | 180 | 184 | 186 | 188 | 188 | 191 | 192 | 193 | 197 | 183 | 153 | 153 | 196 | 178 | 135 | 82 | 140 | 142 | 147 | 176 | 199 | 200 | 199 | 197 | 171 | 173 | 176 | 179 | 182 | 185 | 187 | 189 | 190 | 193 | 193 | 195 | 214 | 190 | 144 | 117 | 178 | 173 | 132 | 77 | 113 | 136 | 105 | 104 | 191 | 201 | 200 | 199 | 172 | 174 | 177 | 181 | 184 | 187 | 188 | 190 | 193 | 193 | 193 | 182 | 187 | 191 | 163 | 121 | 172 | 166 | 119 | 80 | 156 | 159 | 104 | 76 | 169 | 203 | 200 | 200 | 173 | 175 | 179 | 182 | 185 | 188 | 190 | 192 | 193 | 195 | 197 | 158 | 120 | 132 | 165 | 118 | 164 | 183 | 117 | 106 | 183 | 154 | 107 | 74 | 166 | 205 | 202 | 202 | 174 | 177 | 180 | 183 | 187 | 189 | 192 | 193 | 194 | 203 | 200 | 163 | 126 | 97 | 146 | 116 | 159 | 191 | 122 | 123 | 183 | 150 | 102 | 76 | 177 | 204 | 203 | 203 | 175 | 179 | 181 | 185 | 188 | 190 | 193 | 193 | 201 | 193 | 192 | 177 | 137 | 95 | 124 | 125 | 104 | 119 | 88 | 120 | 197 | 160 | 97 | 84 | 190 | 204 | 204 | 205 | 177 | 180 | 183 | 187 | 189 | 192 | 194 | 196 | 203 | 176 | 172 | 175 | 135 | 92 | 101 | 129 | 77 | 27 | 64 | 133 | 174 | 133 | 74 | 98 | 201 | 206 | 206 | 206 | 178 | 182 | 185 | 187 | 189 | 192 | 194 | 201 | 202 | 178 | 153 | 166 | 135 | 87 | 86 | 121 | 88 | 27 | 69 | 154 | 158 | 114 | 81 | 132 | 208 | 207 | 208 | 208 | 178 | 181 | 184 | 187 | 190 | 194 | 194 | 208 | 204 | 176 | 139 | 165 | 131 | 88 | 79 | 112 | 95 | 63 | 127 | 162 | 163 | 147 | 122 | 115 | 198 | 208 | 209 | 209 | 178 | 181 | 185 | 188 | 191 | 195 | 196 | 210 | 203 | 172 | 136 | 150 | 127 | 89 | 76 | 131 | 118 | 118 | 188 | 177 | 153 | 133 | 113 | 88 | 168 | 212 | 210 | 210 | 180 | 182 | 185 | 189 | 192 | 195 | 197 | 213 | 202 | 173 | 142 | 135 | 128 | 109 | 76 | 151 | 146 | 168 | 194 | 179 | 152 | 119 | 98 | 84 | 171 | 212 | 210 | 211 | 181 | 182 | 186 | 189 | 193 | 196 | 199 | 213 | 202 | 180 | 159 | 133 | 116 | 111 | 70 | 149 | 183 | 204 | 193 | 170 | 143 | 110 | 89 | 93 | 192 | 213 | 211 | 211 | 181 | 184 | 187 | 190 | 194 | 196 | 200 | 214 | 203 | 183 | 166 | 149 | 119 | 104 | 109 | 188 | 205 | 209 | 189 | 160 | 133 | 102 | 85 | 108 | 206 | 213 | 213 | 212 | 182 | 185 | 188 | 191 | 194 | 196 | 200 | 211 | 203 | 185 | 171 | 164 | 140 | 122 | 127 | 187 | 206 | 201 | 173 | 142 | 119 | 95 | 85 | 147 | 214 | 214 | 215 | 214 | 181 | 185 | 187 | 190 | 193 | 196 | 199 | 204 | 200 | 190 | 176 | 169 | 148 | 127 | 124 | 183 | 205 | 189 | 157 | 128 | 108 | 89 | 103 | 196 | 214 | 214 | 216 | 215 | 181 | 184 | 187 | 191 | 193 | 196 | 197 | 200 | 199 | 192 | 176 | 171 | 154 | 131 | 131 | 173 | 199 | 174 | 145 | 119 | 99 | 82 | 143 | 214 | 213 | 214 | 215 | 214 | 181 | 184 | 187 | 191 | 194 | 196 | 198 | 200 | 202 | 194 | 177 | 167 | 151 | 131 | 133 | 168 | 176 | 153 | 132 | 109 | 92 | 89 | 181 | 213 | 213 | 213 | 213 | 214 | 182 | 185 | 188 | 192 | 194 | 196 | 198 | 200 | 203 | 191 | 172 | 162 | 142 | 124 | 137 | 161 | 150 | 137 | 117 | 98 | 87 | 123 | 207 | 213 | 214 | 214 | 214 | 214 | 184 | 186 | 189 | 192 | 194 | 196 | 198 | 197 | 203 | 182 | 165 | 151 | 129 | 123 | 143 | 151 | 134 | 127 | 107 | 92 | 94 | 177 | 214 | 214 | 214 | 214 | 215 | 215 | 184 | 185 | 189 | 192 | 195 | 197 | 194 | 201 | 202 | 178 | 166 | 147 | 132 | 134 | 150 | 145 | 130 | 116 | 100 | 88 | 145 | 209 | 214 | 214 | 215 | 215 | 216 | 216 | 184 | 186 | 189 | 193 | 196 | 198 | 194 | 209 | 207 | 184 | 175 | 148 | 137 | 148 | 153 | 133 | 122 | 100 | 87 | 110 | 195 | 215 | 215 | 214 | 215 | 215 | 216 | 216 | 180 | 184 | 187 | 191 | 194 | 196 | 192 | 206 | 202 | 186 | 181 | 152 | 146 | 155 | 143 | 121 | 106 | 88 | 82 | 161 | 213 | 214 | 216 | 215 | 216 | 216 | 216 | 217 | 130 | 134 | 139 | 143 | 147 | 146 | 173 | 200 | 187 | 178 | 174 | 150 | 143 | 149 | 138 | 108 | 94 | 81 | 94 | 178 | 198 | 201 | 204 | 206 | 208 | 209 | 211 | 211 | 99 | 101 | 100 | 100 | 100 | 97 | 163 | 200 | 179 | 161 | 150 | 138 | 138 | 151 | 132 | 97 | 90 | 77 | 88 | 117 | 123 | 127 | 129 | 134 | 145 | 152 | 156 | 179 | 105 | 106 | 105 | 104 | 104 | 104 | 175 | 199 | 178 | 152 | 136 | 130 | 136 | 150 | 118 | 92 | 85 | 76 | 92 | 105 | 105 | 108 | 133 | 163 | 157 | 163 | 164 | 179 |
Xy_original.info(verbose=True)
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pixel109 int64 110 pixel110 int64 111 pixel111 int64 112 pixel112 int64 113 pixel113 int64 114 pixel114 int64 115 pixel115 int64 116 pixel116 int64 117 pixel117 int64 118 pixel118 int64 119 pixel119 int64 120 pixel120 int64 121 pixel121 int64 122 pixel122 int64 123 pixel123 int64 124 pixel124 int64 125 pixel125 int64 126 pixel126 int64 127 pixel127 int64 128 pixel128 int64 129 pixel129 int64 130 pixel130 int64 131 pixel131 int64 132 pixel132 int64 133 pixel133 int64 134 pixel134 int64 135 pixel135 int64 136 pixel136 int64 137 pixel137 int64 138 pixel138 int64 139 pixel139 int64 140 pixel140 int64 141 pixel141 int64 142 pixel142 int64 143 pixel143 int64 144 pixel144 int64 145 pixel145 int64 146 pixel146 int64 147 pixel147 int64 148 pixel148 int64 149 pixel149 int64 150 pixel150 int64 151 pixel151 int64 152 pixel152 int64 153 pixel153 int64 154 pixel154 int64 155 pixel155 int64 156 pixel156 int64 157 pixel157 int64 158 pixel158 int64 159 pixel159 int64 160 pixel160 int64 161 pixel161 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int64 267 pixel267 int64 268 pixel268 int64 269 pixel269 int64 270 pixel270 int64 271 pixel271 int64 272 pixel272 int64 273 pixel273 int64 274 pixel274 int64 275 pixel275 int64 276 pixel276 int64 277 pixel277 int64 278 pixel278 int64 279 pixel279 int64 280 pixel280 int64 281 pixel281 int64 282 pixel282 int64 283 pixel283 int64 284 pixel284 int64 285 pixel285 int64 286 pixel286 int64 287 pixel287 int64 288 pixel288 int64 289 pixel289 int64 290 pixel290 int64 291 pixel291 int64 292 pixel292 int64 293 pixel293 int64 294 pixel294 int64 295 pixel295 int64 296 pixel296 int64 297 pixel297 int64 298 pixel298 int64 299 pixel299 int64 300 pixel300 int64 301 pixel301 int64 302 pixel302 int64 303 pixel303 int64 304 pixel304 int64 305 pixel305 int64 306 pixel306 int64 307 pixel307 int64 308 pixel308 int64 309 pixel309 int64 310 pixel310 int64 311 pixel311 int64 312 pixel312 int64 313 pixel313 int64 314 pixel314 int64 315 pixel315 int64 316 pixel316 int64 317 pixel317 int64 318 pixel318 int64 319 pixel319 int64 320 pixel320 int64 321 pixel321 int64 322 pixel322 int64 323 pixel323 int64 324 pixel324 int64 325 pixel325 int64 326 pixel326 int64 327 pixel327 int64 328 pixel328 int64 329 pixel329 int64 330 pixel330 int64 331 pixel331 int64 332 pixel332 int64 333 pixel333 int64 334 pixel334 int64 335 pixel335 int64 336 pixel336 int64 337 pixel337 int64 338 pixel338 int64 339 pixel339 int64 340 pixel340 int64 341 pixel341 int64 342 pixel342 int64 343 pixel343 int64 344 pixel344 int64 345 pixel345 int64 346 pixel346 int64 347 pixel347 int64 348 pixel348 int64 349 pixel349 int64 350 pixel350 int64 351 pixel351 int64 352 pixel352 int64 353 pixel353 int64 354 pixel354 int64 355 pixel355 int64 356 pixel356 int64 357 pixel357 int64 358 pixel358 int64 359 pixel359 int64 360 pixel360 int64 361 pixel361 int64 362 pixel362 int64 363 pixel363 int64 364 pixel364 int64 365 pixel365 int64 366 pixel366 int64 367 pixel367 int64 368 pixel368 int64 369 pixel369 int64 370 pixel370 int64 371 pixel371 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Xy_original.describe()
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27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.00000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 | 27455.000000 |
| mean | 12.318813 | 145.419377 | 148.500273 | 151.247714 | 153.546531 | 156.210891 | 158.411255 | 160.472154 | 162.339683 | 163.954799 | 165.533673 | 166.685522 | 167.811983 | 168.495647 | 169.310872 | 169.956948 | 170.460463 | 170.716518 | 170.872701 | 170.808887 | 170.481442 | 169.979749 | 169.264506 | 168.144127 | 166.936660 | 165.765944 | 163.620725 | 161.933600 | 161.349117 | 147.146858 | 150.284502 | 152.941978 | 155.415043 | 158.068986 | 160.229576 | 162.345802 | 164.291167 | 165.736332 | 166.991732 | 168.503187 | 169.287307 | 169.497032 | 170.483555 | 170.871135 | 170.719869 | 170.966746 | 171.462211 | 171.149700 | 171.330905 | 171.165762 | 170.522928 | 169.719286 | 168.686141 | 167.347478 | 165.249026 | 163.697469 | 163.000692 | 149.015261 | 152.235658 | 154.769004 | 157.313094 | 159.958405 | 162.161610 | 164.117137 | 166.232708 | 167.487598 | 168.327445 | 169.971044 | 170.829612 | 170.275068 | 170.515899 | 169.761464 | 168.849317 | 169.004881 | 169.285995 | 169.316846 | 170.031870 | 170.758441 | 170.891131 | 170.767729 | 169.811182 | 168.659188 | 166.789547 | 165.502604 | 164.464615 | 150.796394 | 154.049390 | 156.568494 | 159.179894 | 161.803205 | 163.928647 | 165.834748 | 167.930432 | 169.131743 | 169.709889 | 170.810490 | 171.719177 | 170.191368 | 169.667565 | 167.190603 | 165.321981 | 164.602295 | 163.880932 | 163.827208 | 165.756037 | 167.947332 | 169.768749 | 170.402659 | 170.087161 | 169.758332 | 168.298088 | 167.258641 | 166.543835 | 152.493498 | 155.773302 | 158.350100 | 161.159060 | 163.587179 | 165.623202 | 167.475833 | 169.657294 | 170.893353 | 170.830013 | 170.895647 | 170.633036 | 168.574431 | 167.321726 | 164.408705 | 161.673101 | 160.192169 | 158.133418 | 157.576361 | 160.449353 | 162.830960 | 166.084320 | 168.327190 | 169.970497 | 170.250009 | 169.586669 | 169.193808 | 168.647059 | 154.230304 | 157.384666 | 159.875833 | 162.873029 | 165.294373 | 167.283045 | 169.199672 | 171.779093 | 172.741832 | 171.561610 | 170.901329 | 169.233327 | 166.393007 | 164.213914 | 161.106829 | 158.347805 | 156.387507 | 154.533309 | 153.534547 | 155.421271 | 157.151047 | 161.582298 | 165.105482 | 168.556656 | 170.161282 | 171.258714 | 171.712329 | 170.605609 | 155.830705 | 158.925806 | 161.461264 | 164.444072 | 166.928101 | 168.903843 | 171.323183 | 173.520670 | 174.071135 | 172.390639 | 170.145948 | 167.581461 | 163.885631 | 160.611328 | 158.018576 | 155.311127 | 153.376434 | 151.855436 | 151.620943 | 153.796685 | 154.422400 | 158.468111 | 163.212275 | 166.433036 | 169.593043 | 172.281515 | 173.850300 | 172.896012 | 157.402003 | 160.520379 | 163.058314 | 166.141213 | 168.598871 | 170.722819 | 173.018794 | 174.443817 | 174.020215 | 171.751703 | 168.351994 | 164.296121 | 161.050810 | 156.629612 | 153.326279 | 151.576216 | 150.739137 | 149.966563 | 150.438062 | 153.271135 | 152.942743 | 155.216682 | 161.491204 | 165.650774 | 168.639628 | 172.264615 | 174.539756 | 174.682134 | 158.846075 | 161.752541 | 164.530249 | 167.580514 | 169.948206 | 172.433764 | 174.474522 | 175.143653 | 173.405682 | 169.977163 | 165.883300 | 161.072919 | 157.550974 | 152.822437 | 148.594573 | 148.244691 | 148.499071 | 147.988199 | 148.529120 | 151.403861 | 151.833946 | 153.425642 | 160.038317 | 165.317101 | 168.145147 | 172.207795 | 175.376325 | 175.928574 | 160.099982 | 162.971444 | 165.856347 | 168.996248 | 171.511892 | 174.004735 | 175.432672 | 175.278164 | 172.270260 | 167.938518 | 162.532107 | 156.678856 | 152.990603 | 148.095502 | 144.542051 | 144.688581 | 145.06090 | 145.205791 | 146.869350 | 148.760117 | 150.335239 | 153.650592 | 160.017483 | 164.604662 | 168.745911 | 172.683701 | 176.080423 | 176.930687 | 161.397414 | 164.299253 | 167.207977 | 170.362411 | 173.080605 | 175.428665 | 176.834201 | 175.620834 | 171.970424 | 165.470479 | 158.158186 | 151.607212 | 147.331269 | 142.878893 | 139.502167 | 140.188527 | 141.137352 | 142.994646 | 145.776871 | 148.646877 | 149.941140 | 153.755928 | 159.616026 | 163.935895 | 169.204699 | 173.785977 | 177.046294 | 178.104498 | 162.579931 | 165.378656 | 168.472810 | 171.594646 | 174.663522 | 176.927299 | 178.096449 | 175.952249 | 171.158878 | 163.103369 | 153.904134 | 146.029430 | 141.161828 | 136.577090 | 133.400073 | 135.594791 | 138.899435 | 142.058168 | 145.066545 | 149.497250 | 151.243671 | 154.257585 | 159.644509 | 163.419851 | 168.667711 | 174.550974 | 178.079293 | 179.142342 | 163.781788 | 166.678674 | 169.987033 | 173.169222 | 176.006920 | 178.192351 | 178.726862 | 176.490038 | 170.169477 | 160.270151 | 149.649463 | 140.813149 | 136.216828 | 131.726061 | 127.942160 | 131.710545 | 137.707958 | 141.114223 | 144.424331 | 148.334985 | 151.056565 | 154.796685 | 159.096740 | 162.625423 | 167.858386 | 174.990530 | 178.557458 | 179.312439 | 164.919140 | 167.928793 | 171.471608 | 174.825970 | 177.054562 | 179.503187 | 179.291022 | 176.500929 | 170.187762 | 158.495429 | 145.924713 | 136.634238 | 131.573229 | 127.617993 | 125.622765 | 130.467856 | 137.432417 | 140.690876 | 143.755382 | 146.837407 | 149.371044 | 153.29175 | 157.372282 | 160.996321 | 166.32373 | 174.621344 | 178.387980 | 179.411801 | 165.862976 | 168.936915 | 172.494555 | 175.736551 | 178.434675 | 180.798543 | 179.938627 | 176.329157 | 169.455072 | 157.910872 | 144.057148 | 133.780696 | 127.059734 | 125.350647 | 126.683446 | 130.524750 | 136.524058 | 140.367620 | 143.143289 | 144.546822 | 145.975924 | 150.059552 | 155.012639 | 159.095975 | 165.904935 | 174.664979 | 178.689710 | 179.810526 | 166.774759 | 169.953633 | 173.398834 | 176.629721 | 179.658095 | 181.766891 | 181.321981 | 177.549882 | 169.748206 | 157.851794 | 143.743216 | 132.160517 | 125.364888 | 124.901985 | 128.924349 | 133.287015 | 137.574904 | 140.161683 | 141.822109 | 142.285449 | 143.300710 | 146.955309 | 152.252122 | 158.019924 | 166.267893 | 174.022874 | 178.723366 | 180.08523 | 167.560554 | 170.725077 | 174.122018 | 177.309051 | 180.472009 | 182.719432 | 182.218394 | 178.913276 | 170.979129 | 158.472701 | 143.462247 | 132.679439 | 126.813586 | 126.825387 | 131.366090 | 135.609506 | 139.074668 | 141.059333 | 140.945766 | 141.111528 | 141.182517 | 144.245602 | 150.105081 | 157.269823 | 166.691204 | 173.993808 | 178.192460 | 179.213732 | 167.993262 | 171.204116 | 174.603387 | 177.97811 | 180.971845 | 182.992533 | 182.511637 | 179.383937 | 171.634675 | 159.379712 | 145.166964 | 134.738809 | 129.977199 | 130.437042 | 133.006374 | 137.059297 | 140.584557 | 141.560408 | 139.732180 | 139.207977 | 138.706356 | 141.691386 | 148.981679 | 158.296813 | 168.453979 | 174.154544 | 177.583792 | 178.938736 | 167.592788 | 171.162994 | 174.589073 | 177.954908 | 180.775997 | 182.331451 | 182.398470 | 179.366272 | 172.345766 | 160.552686 | 148.194354 | 137.927773 | 133.710727 | 134.238864 | 136.713641 | 138.885704 | 141.611801 | 141.184593 | 138.126352 | 136.040102 | 136.096012 | 141.095975 | 150.249900 | 161.824185 | 170.249499 | 175.461519 | 178.353451 | 178.846221 | 166.415043 | 170.231725 | 173.948534 | 177.244400 | 179.818977 | 181.153597 | 181.888946 | 179.719250 | 172.965544 | 162.529776 | 151.198980 | 141.735130 | 137.952103 | 138.750137 | 140.775779 | 142.422837 | 142.979894 | 140.340776 | 136.618321 | 133.168858 | 135.165726 | 142.817374 | 154.137571 | 165.153597 | 172.243489 | 177.444655 | 178.950756 | 178.269823 | 164.713313 | 168.328647 | 172.190457 | 175.241741 | 177.782080 | 179.361537 | 180.014751 | 178.739064 | 172.904134 | 163.943908 | 154.005828 | 145.599417 | 141.973593 | 141.274558 | 143.146786 | 144.671426 | 142.871827 | 138.371335 | 133.792388 | 132.228009 | 137.290366 | 147.177308 | 159.438572 | 168.499690 | 174.297068 | 177.513131 | 177.871754 | 176.596977 | 162.829139 | 166.214533 | 169.220980 | 172.075724 | 174.945984 | 176.700273 | 177.484320 | 176.777454 | 171.878638 | 163.918084 | 155.588854 | 148.586633 | 144.153815 | 142.741067 | 144.292296 | 144.697286 | 141.352832 | 135.527481 | 132.314223 | 134.889201 | 141.741978 | 151.980951 | 162.910909 | 169.837370 | 174.045201 | 175.976252 | 175.761027 | 174.493753 | 161.250410 | 164.043307 | 166.194136 | 168.697359 | 171.239374 | 173.050592 | 174.268075 | 173.820652 | 169.845748 | 163.176070 | 155.916044 | 149.891932 | 144.926935 | 142.940484 | 143.330177 | 142.309415 | 138.403606 | 134.085959 | 134.029466 | 138.084793 | 146.508468 | 156.861373 | 165.039701 | 170.151958 | 173.581461 | 174.240794 | 173.489710 | 172.680568 | 159.230195 | 161.710253 | 163.483701 | 165.520779 | 167.203023 | 169.009033 | 170.133273 | 169.651175 | 166.728829 | 161.822983 | 155.839191 | 149.785722 | 144.802622 | 142.034129 | 141.457585 | 139.342415 | 135.820543 | 134.204844 | 135.291131 | 140.705190 | 150.273539 | 159.804043 | 166.696194 | 170.803096 | 172.547405 | 172.215516 | 171.403642 | 170.865635 | 155.466764 | 157.719177 | 159.16802 | 161.077581 | 162.395083 | 163.957312 | 164.415407 | 163.768239 | 161.939938 | 158.738445 | 154.004771 | 147.938518 | 143.131634 | 139.984192 | 138.519323 | 136.431069 | 134.377563 | 134.012129 | 136.160153 | 142.979712 | 153.355564 | 161.015917 | 166.234456 | 169.803132 | 170.497359 | 169.920306 | 169.202295 | 168.562921 | 151.226334 | 152.917137 | 154.173812 | 156.229576 | 157.714442 | 159.216026 | 159.278638 | 158.280495 | 157.055655 | 155.039920 | 150.594500 | 145.369368 | 140.655946 | 137.464761 | 135.660244 | 133.949117 | 133.231943 | 133.220215 | 137.090912 | 145.385139 | 155.083992 | 160.937643 | 165.098197 | 167.691750 | 168.030195 | 167.698197 | 166.982153 | 166.277509 | 147.123803 | 148.348607 | 149.754617 | 151.619304 | 153.333710 | 154.011765 | 153.487598 | 152.768858 | 151.640248 | 149.348534 | 146.573484 | 142.390384 | 138.580222 | 136.411036 | 134.006119 | 131.549044 | 131.949918 | 134.583755 | 139.361974 | 148.115862 | 155.095465 | 160.346858 | 163.915607 | 165.364414 | 165.911273 | 165.182080 | 164.407977 | 163.488254 | 143.407758 | 144.189474 | 145.711637 | 147.660718 | 149.019414 | 148.670843 | 148.185212 | 147.298926 | 146.286323 | 144.027062 | 142.966017 | 139.769550 | 137.071572 | 135.277181 | 131.922783 | 130.232235 | 132.046367 | 135.289237 | 141.104863 | 147.495611 | 153.325806 | 159.125332 | 161.969259 | 162.736696 | 162.906137 | 161.966454 | 161.137898 | 159.824731 |
| std | 7.287552 | 41.358555 | 39.942152 | 39.056286 | 38.595247 | 37.111165 | 36.125579 | 35.016392 | 33.661998 | 32.651607 | 31.279244 | 30.558445 | 29.771007 | 29.329251 | 28.620248 | 27.961255 | 27.053544 | 26.763535 | 26.307419 | 26.088459 | 26.475963 | 26.940885 | 27.871515 | 29.368386 | 30.906718 | 31.902723 | 34.303747 | 35.991306 | 36.571064 | 41.555429 | 40.094304 | 39.427215 | 38.686176 | 37.242459 | 36.373576 | 35.242915 | 33.899171 | 32.759395 | 31.656140 | 30.833853 | 30.285472 | 30.014701 | 29.488345 | 28.877079 | 28.385090 | 28.071306 | 27.385837 | 27.169745 | 27.054699 | 27.287977 | 28.346384 | 29.614235 | 31.048117 | 32.299271 | 34.411090 | 35.837702 | 36.584178 | 41.778308 | 40.241183 | 39.766083 | 38.811304 | 37.558850 | 36.738919 | 35.524369 | 34.143523 | 33.366659 | 32.706590 | 31.540195 | 30.919631 | 31.734199 | 31.376934 | 31.711126 | 32.105972 | 31.567277 | 30.990092 | 30.646945 | 29.567699 | 29.121604 | 29.950303 | 30.060839 | 31.848742 | 33.397800 | 34.833426 | 36.160581 | 37.130147 | 42.029500 | 40.510134 | 39.982096 | 38.990179 | 37.831990 | 37.013307 | 35.994198 | 34.803070 | 34.186032 | 33.590485 | 33.023394 | 32.332933 | 33.489875 | 34.131590 | 35.504633 | 36.163043 | 36.267805 | 36.035518 | 35.554383 | 33.995554 | 32.618142 | 32.169005 | 31.606215 | 33.091450 | 33.799247 | 35.040408 | 36.543966 | 37.008573 | 42.230596 | 40.722846 | 40.269497 | 39.286656 | 38.055911 | 37.175998 | 36.414078 | 35.364973 | 35.137786 | 34.791087 | 35.670272 | 35.450400 | 37.389018 | 38.142135 | 39.028650 | 40.237810 | 40.801754 | 41.360760 | 40.621785 | 38.440031 | 37.704805 | 35.661038 | 34.722326 | 34.627536 | 34.460804 | 35.585550 | 35.856267 | 36.379407 | 42.520489 | 41.141604 | 40.665904 | 39.471974 | 38.243671 | 37.468092 | 36.637765 | 36.111578 | 35.828897 | 36.202284 | 37.995085 | 39.124282 | 40.007543 | 41.764678 | 43.127948 | 43.690112 | 44.329521 | 44.391484 | 44.608277 | 42.813879 | 42.149745 | 39.864605 | 38.087663 | 36.059258 | 35.490495 | 35.179224 | 34.845038 | 35.917254 | 42.686980 | 41.425780 | 41.015447 | 39.706068 | 38.707245 | 37.621740 | 36.678723 | 36.607763 | 36.198828 | 37.750438 | 40.092848 | 41.412790 | 42.751692 | 44.994999 | 46.082327 | 46.480810 | 47.257773 | 46.664866 | 46.438683 | 44.591409 | 44.680532 | 42.393541 | 40.104588 | 38.489088 | 36.673132 | 34.925551 | 33.608328 | 34.730713 | 42.783174 | 41.585817 | 41.339247 | 39.894171 | 39.207617 | 38.036799 | 37.430985 | 37.344942 | 37.873361 | 39.445123 | 42.723254 | 44.317912 | 44.981873 | 47.585207 | 47.755564 | 48.124048 | 48.186147 | 46.947563 | 46.768885 | 45.488701 | 46.041979 | 45.495555 | 42.661805 | 40.823591 | 39.054250 | 36.386452 | 34.316857 | 33.832094 | 42.986343 | 41.969817 | 41.541042 | 40.412780 | 39.693188 | 38.378560 | 37.851723 | 38.009317 | 38.899522 | 41.045157 | 44.584033 | 46.041528 | 46.827974 | 49.090368 | 49.440601 | 48.585840 | 47.982886 | 47.585353 | 47.303372 | 45.387759 | 46.301803 | 46.965949 | 44.785083 | 42.550474 | 41.179438 | 37.335756 | 34.736565 | 33.794241 | 43.334310 | 42.460100 | 42.004419 | 40.955459 | 40.292639 | 38.197912 | 38.107000 | 38.597378 | 39.889654 | 42.921361 | 45.329351 | 46.935892 | 48.345221 | 49.573104 | 49.508355 | 48.611495 | 47.86773 | 47.759402 | 47.854134 | 47.118160 | 47.278849 | 48.001876 | 45.920356 | 43.625525 | 40.753489 | 38.167379 | 34.732093 | 34.434877 | 43.667002 | 42.883307 | 42.140379 | 41.090449 | 40.344930 | 38.540337 | 38.624698 | 39.290984 | 41.271550 | 44.798345 | 47.068909 | 48.107023 | 48.774274 | 49.837125 | 49.600538 | 48.367929 | 48.565419 | 48.543205 | 48.931035 | 48.194725 | 47.963615 | 48.720651 | 46.713019 | 45.092546 | 41.522295 | 38.880070 | 34.767673 | 34.245483 | 43.917995 | 43.255520 | 42.113002 | 41.174804 | 40.166780 | 38.944201 | 39.261357 | 40.121785 | 42.336360 | 45.966951 | 48.836116 | 49.928701 | 50.157866 | 51.181726 | 50.046819 | 48.902870 | 50.326335 | 50.267419 | 49.867515 | 48.503102 | 48.419363 | 48.906813 | 46.980377 | 46.057195 | 43.246102 | 39.527382 | 36.103995 | 34.546056 | 43.933083 | 43.136405 | 41.918544 | 41.026460 | 40.054835 | 39.351904 | 40.369145 | 41.172182 | 43.104622 | 47.071433 | 49.937849 | 50.648017 | 50.730912 | 51.422481 | 51.252661 | 49.846747 | 50.943771 | 51.323190 | 50.301512 | 49.146169 | 49.413640 | 48.697965 | 47.608506 | 47.590411 | 44.747602 | 39.831635 | 37.198715 | 35.792995 | 44.104308 | 43.411408 | 42.053777 | 41.172844 | 40.447399 | 40.126154 | 41.186312 | 41.730232 | 44.068295 | 47.941677 | 50.020959 | 50.989468 | 50.643443 | 51.144372 | 50.956894 | 51.106043 | 51.800618 | 51.472043 | 50.558362 | 50.179273 | 50.397042 | 49.67878 | 48.670011 | 49.210489 | 46.51766 | 41.778188 | 38.464118 | 37.841808 | 44.367527 | 43.773834 | 42.318724 | 41.635380 | 41.088562 | 40.571813 | 41.006124 | 42.624014 | 45.482039 | 48.306048 | 49.573526 | 49.748005 | 50.133302 | 50.234619 | 50.402747 | 51.829862 | 52.612133 | 52.246332 | 51.675397 | 51.558525 | 50.962162 | 51.483887 | 50.773570 | 50.991611 | 47.702531 | 43.271017 | 39.812806 | 38.628440 | 44.734481 | 44.075152 | 42.669953 | 42.365795 | 41.600537 | 41.016695 | 41.313907 | 43.158295 | 46.164683 | 47.751418 | 49.166332 | 49.594843 | 49.568748 | 50.732567 | 51.652906 | 53.629683 | 53.745496 | 53.890071 | 52.968585 | 51.923949 | 51.924891 | 53.238049 | 52.730098 | 52.457900 | 49.444810 | 44.966384 | 42.013979 | 40.44858 | 45.122056 | 44.306051 | 43.161115 | 43.096720 | 41.986319 | 40.998546 | 41.529168 | 43.223175 | 45.952033 | 47.577955 | 48.153488 | 48.497473 | 49.906946 | 51.492235 | 53.677052 | 54.936786 | 54.970760 | 55.541516 | 53.431099 | 52.101316 | 52.537648 | 53.588788 | 54.351671 | 53.491590 | 50.757713 | 47.083811 | 44.029288 | 42.583125 | 45.326658 | 44.633778 | 43.679628 | 43.60682 | 42.246818 | 41.032229 | 42.145044 | 43.617781 | 46.053796 | 47.823937 | 48.127729 | 48.857155 | 50.477645 | 53.437553 | 54.795758 | 56.111923 | 55.939864 | 55.202311 | 53.209616 | 51.239624 | 52.316325 | 53.598076 | 55.917288 | 54.188452 | 51.553497 | 48.303020 | 45.779313 | 44.672685 | 45.870159 | 45.082930 | 44.360838 | 44.150074 | 42.795383 | 42.149035 | 42.765427 | 44.633559 | 46.203566 | 48.606101 | 48.720199 | 49.034127 | 50.544436 | 53.504578 | 55.221908 | 56.935795 | 55.720427 | 54.249338 | 51.360469 | 50.905418 | 52.647791 | 54.931210 | 57.197665 | 55.032350 | 52.927331 | 49.306868 | 47.452352 | 46.392674 | 46.918532 | 45.989082 | 45.178991 | 44.715976 | 43.869189 | 43.122641 | 43.117747 | 44.834245 | 46.514149 | 48.493541 | 49.104975 | 48.656682 | 50.991415 | 52.701899 | 54.613006 | 55.513007 | 55.232457 | 52.678025 | 50.329868 | 50.896196 | 53.022342 | 56.653193 | 58.076049 | 56.522666 | 53.579719 | 49.816612 | 48.095602 | 47.808138 | 47.792090 | 47.110084 | 46.368032 | 45.535543 | 44.540787 | 43.644227 | 43.698046 | 44.923752 | 46.431540 | 47.999209 | 48.757032 | 48.673817 | 51.269215 | 52.091283 | 53.910823 | 54.318384 | 53.650325 | 51.710436 | 50.754592 | 51.569382 | 54.977137 | 58.445599 | 57.787255 | 56.791071 | 53.546746 | 50.889420 | 50.040698 | 50.334770 | 48.663201 | 48.316535 | 47.898254 | 47.072906 | 46.117702 | 45.408085 | 44.837023 | 45.749846 | 47.272157 | 48.650843 | 49.054784 | 49.201845 | 50.264820 | 51.128225 | 52.156931 | 53.078386 | 52.925047 | 51.972113 | 50.556698 | 53.450780 | 57.449880 | 59.106513 | 58.592734 | 56.539071 | 54.082713 | 52.595619 | 52.119543 | 52.660055 | 49.796694 | 49.350564 | 49.771640 | 49.272314 | 48.600859 | 47.715438 | 47.166406 | 47.469815 | 48.921759 | 49.419971 | 49.601527 | 49.164146 | 49.704754 | 49.929268 | 51.045191 | 52.393291 | 52.352515 | 51.888322 | 52.700578 | 56.292015 | 58.698748 | 59.895568 | 58.865256 | 57.201302 | 55.484131 | 54.840932 | 54.494393 | 54.538267 | 50.457155 | 50.507701 | 51.209278 | 50.398698 | 50.713131 | 50.258163 | 49.240510 | 49.429988 | 50.066609 | 50.291321 | 50.107081 | 49.593373 | 49.635372 | 49.565189 | 50.493717 | 51.953341 | 52.262539 | 53.060074 | 55.216992 | 58.386391 | 60.544561 | 60.368488 | 59.313495 | 57.399857 | 56.667567 | 56.966751 | 56.824345 | 56.402894 | 51.850388 | 51.782992 | 52.52728 | 52.416922 | 52.901234 | 52.313785 | 52.091616 | 51.416181 | 51.800803 | 51.709716 | 51.478392 | 50.572205 | 49.928662 | 49.093708 | 50.528777 | 52.251552 | 53.375389 | 55.731283 | 57.696790 | 60.709611 | 61.767661 | 61.271725 | 59.730246 | 58.566942 | 58.248748 | 58.624993 | 58.745069 | 58.482183 | 53.092065 | 53.306515 | 53.920602 | 53.548103 | 53.525993 | 53.310340 | 53.612331 | 53.632965 | 54.082230 | 53.256292 | 53.078511 | 51.819515 | 50.874587 | 49.963778 | 51.206310 | 53.437942 | 55.607151 | 58.007756 | 60.902711 | 62.301631 | 62.526226 | 61.889131 | 60.529388 | 60.523591 | 60.360628 | 60.566993 | 60.968961 | 60.688955 | 54.035002 | 53.633619 | 54.747464 | 54.306448 | 54.008754 | 54.752529 | 54.371094 | 55.134335 | 56.230943 | 55.466736 | 54.602459 | 53.765855 | 52.422790 | 52.008350 | 53.572756 | 55.869388 | 57.586028 | 60.373218 | 63.301768 | 63.300608 | 63.511566 | 62.551522 | 61.833119 | 62.210750 | 62.163516 | 61.958245 | 61.950177 | 62.403709 | 54.406116 | 53.658786 | 54.710497 | 54.613724 | 54.351761 | 55.233710 | 55.904715 | 57.155523 | 57.965677 | 57.393379 | 56.689603 | 56.118823 | 54.680900 | 55.278778 | 56.384076 | 58.111783 | 59.204300 | 62.553694 | 63.751194 | 65.512894 | 64.427412 | 63.708507 | 63.738316 | 63.444008 | 63.509210 | 63.298721 | 63.610415 | 64.396846 |
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0.000000 | 0.000000 | 0.000000 | 0.000000 | 6.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 3.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 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0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
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153.000000 | 154.000000 | 155.000000 | 155.000000 | 155.000000 | 154.000000 | 152.000000 | 151.000000 | 150.000000 | 127.000000 | 132.000000 | 136.000000 | 139.000000 | 142.000000 | 145.000000 | 148.000000 | 150.000000 | 151.000000 | 151.000000 | 152.000000 | 154.000000 | 151.000000 | 151.000000 | 147.000000 | 145.000000 | 144.000000 | 143.000000 | 143.000000 | 147.000000 | 150.000000 | 153.000000 | 155.000000 | 155.000000 | 155.000000 | 154.000000 | 153.000000 | 152.000000 | 129.000000 | 133.000000 | 138.000000 | 141.000000 | 144.000000 | 147.000000 | 149.000000 | 152.000000 | 152.000000 | 152.000000 | 151.000000 | 151.000000 | 148.000000 | 146.000000 | 142.000000 | 138.000000 | 136.000000 | 132.000000 | 132.000000 | 137.000000 | 141.000000 | 147.000000 | 151.000000 | 154.000000 | 155.000000 | 155.000000 | 155.000000 | 154.000000 | 130.000000 | 135.000000 | 139.000000 | 142.000000 | 146.000000 | 148.000000 | 151.000000 | 153.000000 | 154.000000 | 151.000000 | 150.000000 | 148.000000 | 144.000000 | 140.000000 | 135.000000 | 131.000000 | 128.000000 | 125.000000 | 123.000000 | 128.000000 | 130.000000 | 139.000000 | 146.000000 | 152.000000 | 154.000000 | 156.000000 | 157.000000 | 156.000000 | 132.000000 | 137.000000 | 140.000000 | 144.000000 | 147.000000 | 150.000000 | 153.000000 | 154.000000 | 155.000000 | 152.000000 | 148.000000 | 145.000000 | 139.000000 | 133.000000 | 129.000000 | 124.000000 | 122.000000 | 119.000000 | 119.000000 | 124.000000 | 124.000000 | 132.000000 | 142.000000 | 147.000000 | 152.000000 | 156.000000 | 159.000000 | 158.000000 | 134.000000 | 138.000000 | 142.000000 | 146.000000 | 149.000000 | 151.000000 | 154.000000 | 155.000000 | 154.000000 | 151.000000 | 145.000000 | 138.000000 | 134.000000 | 126.000000 | 122.000000 | 119.000000 | 117.000000 | 116.000000 | 117.000000 | 123.000000 | 121.000000 | 125.000000 | 138.000000 | 145.000000 | 151.000000 | 156.000000 | 159.000000 | 159.000000 | 135.000000 | 140.000000 | 143.000000 | 147.000000 | 150.000000 | 153.000000 | 155.000000 | 155.000000 | 152.000000 | 147.000000 | 140.000000 | 133.000000 | 128.000000 | 119.000000 | 114.000000 | 114.000000 | 115.000000 | 113.000000 | 114.000000 | 120.000000 | 119.000000 | 121.000000 | 133.000000 | 144.000000 | 150.000000 | 155.000000 | 160.000000 | 160.000000 | 136.000000 | 141.000000 | 145.000000 | 149.000000 | 152.000000 | 154.000000 | 155.000000 | 154.000000 | 150.000000 | 143.000000 | 135.000000 | 126.000000 | 121.000000 | 113.000000 | 108.000000 | 110.000000 | 111.00000 | 110.000000 | 111.000000 | 114.000000 | 116.000000 | 119.000000 | 131.000000 | 141.000000 | 149.000000 | 155.000000 | 160.000000 | 161.000000 | 138.000000 | 142.000000 | 146.000000 | 150.000000 | 153.000000 | 155.000000 | 156.000000 | 154.000000 | 148.000000 | 138.000000 | 127.000000 | 118.000000 | 113.000000 | 107.000000 | 103.000000 | 106.000000 | 106.000000 | 107.000000 | 109.000000 | 113.000000 | 115.000000 | 119.000000 | 129.000000 | 139.000000 | 149.000000 | 157.000000 | 161.000000 | 162.000000 | 139.000000 | 143.000000 | 147.000000 | 151.000000 | 155.000000 | 156.000000 | 156.000000 | 153.000000 | 146.000000 | 134.000000 | 120.000000 | 109.000000 | 104.000000 | 98.000000 | 96.000000 | 100.000000 | 103.000000 | 104.000000 | 107.000000 | 114.000000 | 116.000000 | 118.000000 | 128.500000 | 136.000000 | 148.000000 | 157.000000 | 162.000000 | 163.000000 | 140.000000 | 145.000000 | 149.000000 | 153.000000 | 156.000000 | 158.000000 | 156.000000 | 153.000000 | 144.000000 | 129.000000 | 114.000000 | 103.000000 | 98.000000 | 93.000000 | 89.000000 | 96.000000 | 101.000000 | 103.000000 | 106.000000 | 112.000000 | 114.000000 | 119.000000 | 126.000000 | 133.000000 | 146.000000 | 157.500000 | 163.000000 | 164.000000 | 141.000000 | 146.000000 | 150.000000 | 154.000000 | 156.000000 | 158.000000 | 157.000000 | 152.000000 | 143.000000 | 125.000000 | 109.000000 | 97.000000 | 93.000000 | 90.000000 | 88.000000 | 93.000000 | 100.000000 | 102.000000 | 106.000000 | 109.000000 | 111.000000 | 116.00000 | 122.000000 | 128.000000 | 143.00000 | 157.000000 | 162.000000 | 164.000000 | 142.000000 | 147.000000 | 151.000000 | 155.000000 | 157.000000 | 159.000000 | 157.000000 | 151.000000 | 140.000000 | 124.000000 | 108.000000 | 97.000000 | 89.000000 | 88.000000 | 90.000000 | 93.000000 | 98.000000 | 101.000000 | 104.000000 | 105.000000 | 107.000000 | 110.000000 | 116.000000 | 122.000000 | 141.000000 | 157.000000 | 163.000000 | 165.000000 | 143.000000 | 148.000000 | 152.000000 | 156.000000 | 158.000000 | 160.000000 | 158.000000 | 152.000000 | 139.000000 | 125.000000 | 108.000000 | 95.000000 | 88.500000 | 87.000000 | 90.000000 | 93.000000 | 98.000000 | 99.000000 | 101.000000 | 103.000000 | 103.000000 | 105.000000 | 110.000000 | 118.000000 | 141.000000 | 156.000000 | 164.000000 | 166.00000 | 144.000000 | 148.000000 | 152.000000 | 156.000000 | 159.000000 | 160.000000 | 158.000000 | 153.000000 | 141.000000 | 125.000000 | 109.000000 | 97.000000 | 90.000000 | 88.000000 | 91.000000 | 94.000000 | 97.000000 | 98.000000 | 101.000000 | 102.000000 | 101.000000 | 102.000000 | 106.000000 | 117.000000 | 139.000000 | 157.000000 | 164.000000 | 165.000000 | 144.000000 | 149.000000 | 153.000000 | 157.00000 | 159.000000 | 161.000000 | 159.000000 | 154.000000 | 142.000000 | 127.000000 | 111.000000 | 99.000000 | 93.000000 | 90.000000 | 91.000000 | 94.000000 | 98.000000 | 99.000000 | 100.000000 | 101.000000 | 99.000000 | 100.000000 | 103.000000 | 117.000000 | 143.000000 | 157.000000 | 163.000000 | 166.000000 | 143.000000 | 148.000000 | 152.000000 | 156.000000 | 159.000000 | 161.000000 | 159.000000 | 154.000000 | 143.000000 | 127.000000 | 114.000000 | 102.500000 | 96.000000 | 94.000000 | 93.000000 | 94.000000 | 100.000000 | 100.000000 | 100.000000 | 98.000000 | 96.000000 | 97.000000 | 102.000000 | 123.000000 | 148.000000 | 160.000000 | 165.000000 | 166.000000 | 140.000000 | 145.000000 | 150.000000 | 155.000000 | 158.000000 | 159.000000 | 159.000000 | 154.000000 | 143.000000 | 129.000000 | 117.000000 | 107.000000 | 100.000000 | 99.000000 | 98.000000 | 100.000000 | 101.000000 | 101.000000 | 100.000000 | 96.000000 | 95.000000 | 97.000000 | 105.000000 | 130.000000 | 154.000000 | 164.000000 | 166.000000 | 166.000000 | 136.000000 | 142.000000 | 147.000000 | 151.000000 | 155.000000 | 156.000000 | 156.000000 | 152.000000 | 143.000000 | 131.000000 | 120.000000 | 110.000000 | 103.000000 | 102.000000 | 103.000000 | 104.000000 | 103.000000 | 100.000000 | 96.000000 | 94.000000 | 95.000000 | 98.000000 | 114.000000 | 142.000000 | 159.000000 | 164.000000 | 165.000000 | 164.000000 | 133.000000 | 138.000000 | 143.000000 | 146.000000 | 150.000000 | 152.000000 | 152.000000 | 149.000000 | 142.000000 | 131.000000 | 121.000000 | 113.000000 | 107.000000 | 105.000000 | 106.000000 | 106.000000 | 102.000000 | 97.000000 | 95.000000 | 95.000000 | 96.000000 | 102.000000 | 120.000000 | 147.000000 | 159.000000 | 162.000000 | 162.000000 | 161.000000 | 129.000000 | 133.000000 | 135.000000 | 139.000000 | 141.000000 | 145.000000 | 146.000000 | 145.000000 | 140.000000 | 130.000000 | 122.000000 | 115.000000 | 109.000000 | 107.000000 | 107.000000 | 104.000000 | 100.000000 | 96.000000 | 95.000000 | 94.000000 | 98.000000 | 107.000000 | 125.000000 | 149.000000 | 158.000000 | 160.000000 | 159.000000 | 159.000000 | 125.000000 | 129.000000 | 129.000000 | 132.000000 | 135.000000 | 138.000000 | 139.000000 | 139.000000 | 135.000000 | 129.000000 | 121.000000 | 115.000000 | 110.000000 | 108.000000 | 106.000000 | 102.000000 | 98.000000 | 96.000000 | 94.000000 | 95.000000 | 100.000000 | 112.000000 | 133.000000 | 151.000000 | 156.000000 | 157.000000 | 156.000000 | 156.000000 | 118.000000 | 120.000000 | 121.00000 | 123.000000 | 125.000000 | 128.000000 | 129.000000 | 128.000000 | 127.000000 | 124.000000 | 119.000000 | 113.000000 | 109.000000 | 107.000000 | 103.000000 | 99.000000 | 96.000000 | 93.000000 | 92.000000 | 94.000000 | 103.000000 | 114.000000 | 132.000000 | 149.000000 | 152.000000 | 152.000000 | 152.000000 | 152.000000 | 111.000000 | 112.000000 | 113.000000 | 115.000000 | 117.000000 | 119.000000 | 119.000000 | 120.000000 | 119.000000 | 119.000000 | 115.000000 | 110.000000 | 106.000000 | 104.000000 | 100.000000 | 96.000000 | 93.000000 | 90.000000 | 90.000000 | 96.000000 | 104.000000 | 116.000000 | 130.000000 | 140.000000 | 144.000000 | 145.000000 | 146.000000 | 147.000000 | 107.000000 | 108.000000 | 109.000000 | 110.000000 | 112.000000 | 113.000000 | 112.000000 | 113.000000 | 111.000000 | 111.000000 | 109.000000 | 106.000000 | 104.000000 | 102.000000 | 97.000000 | 92.000000 | 91.000000 | 90.000000 | 90.000000 | 98.000000 | 104.000000 | 115.000000 | 127.000000 | 132.000000 | 137.000000 | 138.000000 | 140.000000 | 138.000000 | 104.000000 | 104.000000 | 105.000000 | 106.000000 | 107.000000 | 107.000000 | 106.000000 | 106.000000 | 105.000000 | 104.000000 | 104.000000 | 102.000000 | 102.000000 | 99.000000 | 94.000000 | 90.000000 | 90.000000 | 89.000000 | 92.000000 | 96.000000 | 103.000000 | 112.000000 | 120.000000 | 125.000000 | 128.000000 | 128.000000 | 128.000000 | 125.500000 |
| 50% | 13.000000 | 150.000000 | 153.000000 | 156.000000 | 158.000000 | 160.000000 | 162.000000 | 164.000000 | 165.000000 | 166.000000 | 167.000000 | 168.000000 | 169.000000 | 170.000000 | 170.000000 | 171.000000 | 171.000000 | 171.000000 | 171.000000 | 171.000000 | 170.000000 | 170.000000 | 170.000000 | 169.000000 | 168.000000 | 168.000000 | 167.000000 | 166.000000 | 165.000000 | 152.000000 | 155.000000 | 157.000000 | 160.000000 | 162.000000 | 164.000000 | 166.000000 | 167.000000 | 168.000000 | 169.000000 | 170.000000 | 171.000000 | 171.000000 | 172.000000 | 172.000000 | 171.000000 | 171.000000 | 172.000000 | 171.000000 | 171.000000 | 171.000000 | 171.000000 | 171.000000 | 170.000000 | 169.000000 | 168.000000 | 167.000000 | 167.000000 | 154.000000 | 157.000000 | 159.000000 | 162.000000 | 164.000000 | 166.000000 | 168.000000 | 169.000000 | 170.000000 | 171.000000 | 172.000000 | 172.000000 | 172.000000 | 172.000000 | 171.000000 | 171.000000 | 171.000000 | 171.000000 | 171.000000 | 171.000000 | 172.000000 | 172.000000 | 172.000000 | 171.000000 | 171.000000 | 170.000000 | 169.000000 | 168.000000 | 156.000000 | 159.000000 | 162.000000 | 164.000000 | 166.000000 | 168.000000 | 170.000000 | 171.000000 | 172.000000 | 172.000000 | 173.000000 | 173.000000 | 173.000000 | 172.000000 | 171.000000 | 169.000000 | 168.000000 | 168.000000 | 168.000000 | 169.000000 | 170.000000 | 172.000000 | 172.000000 | 172.000000 | 172.000000 | 171.000000 | 171.000000 | 170.000000 | 158.000000 | 161.000000 | 164.000000 | 166.000000 | 168.000000 | 170.000000 | 171.000000 | 173.000000 | 174.000000 | 174.000000 | 174.000000 | 174.000000 | 172.000000 | 171.000000 | 169.000000 | 167.000000 | 166.000000 | 164.000000 | 164.000000 | 165.000000 | 168.000000 | 170.000000 | 172.000000 | 173.000000 | 173.000000 | 173.000000 | 172.000000 | 172.000000 | 160.000000 | 163.000000 | 165.000000 | 168.000000 | 170.000000 | 171.000000 | 173.000000 | 175.000000 | 176.000000 | 175.000000 | 175.000000 | 174.000000 | 171.000000 | 169.000000 | 167.000000 | 164.000000 | 163.000000 | 161.000000 | 160.000000 | 162.000000 | 164.000000 | 168.000000 | 171.000000 | 173.000000 | 174.000000 | 174.000000 | 174.000000 | 174.000000 | 162.000000 | 165.000000 | 167.000000 | 170.000000 | 172.000000 | 173.000000 | 175.000000 | 177.000000 | 178.000000 | 176.000000 | 175.000000 | 173.000000 | 170.000000 | 167.000000 | 165.000000 | 161.000000 | 160.000000 | 157.000000 | 158.000000 | 160.000000 | 162.000000 | 166.000000 | 170.000000 | 172.000000 | 174.000000 | 175.000000 | 176.000000 | 175.000000 | 163.000000 | 166.000000 | 169.000000 | 172.000000 | 174.000000 | 175.000000 | 177.000000 | 179.000000 | 178.000000 | 177.000000 | 175.000000 | 171.000000 | 167.000000 | 163.000000 | 159.000000 | 157.000000 | 156.000000 | 154.000000 | 156.000000 | 159.000000 | 160.000000 | 164.000000 | 169.000000 | 173.000000 | 174.000000 | 176.000000 | 177.000000 | 177.000000 | 165.000000 | 168.000000 | 170.000000 | 173.000000 | 175.000000 | 177.000000 | 179.000000 | 180.000000 | 178.000000 | 175.000000 | 172.000000 | 167.000000 | 163.000000 | 159.000000 | 153.000000 | 152.000000 | 152.000000 | 151.000000 | 152.000000 | 156.000000 | 158.000000 | 162.000000 | 169.000000 | 173.000000 | 175.000000 | 177.000000 | 178.000000 | 178.000000 | 166.000000 | 169.000000 | 172.000000 | 175.000000 | 177.000000 | 179.000000 | 180.000000 | 179.000000 | 177.000000 | 174.000000 | 168.000000 | 162.000000 | 157.000000 | 152.000000 | 148.000000 | 147.000000 | 147.00000 | 147.000000 | 151.000000 | 153.000000 | 156.000000 | 162.000000 | 169.000000 | 173.000000 | 176.000000 | 178.000000 | 179.000000 | 179.000000 | 168.000000 | 171.000000 | 174.000000 | 176.000000 | 179.000000 | 180.000000 | 181.000000 | 180.000000 | 177.000000 | 171.000000 | 163.000000 | 155.000000 | 151.000000 | 144.000000 | 140.000000 | 142.000000 | 142.000000 | 144.000000 | 149.000000 | 152.000000 | 155.000000 | 162.000000 | 169.000000 | 174.000000 | 177.000000 | 179.000000 | 180.000000 | 181.000000 | 169.000000 | 172.000000 | 175.000000 | 178.000000 | 180.000000 | 181.000000 | 182.000000 | 180.000000 | 176.000000 | 169.000000 | 158.000000 | 148.000000 | 143.000000 | 137.000000 | 133.000000 | 136.000000 | 139.000000 | 144.000000 | 147.000000 | 153.000000 | 156.000000 | 162.000000 | 169.000000 | 173.000000 | 177.000000 | 180.000000 | 182.000000 | 182.000000 | 171.000000 | 174.000000 | 177.000000 | 179.000000 | 182.000000 | 183.000000 | 183.000000 | 182.000000 | 175.000000 | 166.000000 | 153.000000 | 141.000000 | 137.000000 | 131.000000 | 126.000000 | 131.000000 | 138.000000 | 142.000000 | 146.000000 | 151.000000 | 156.000000 | 162.000000 | 168.000000 | 173.000000 | 177.000000 | 181.000000 | 183.000000 | 182.000000 | 172.000000 | 175.000000 | 178.000000 | 181.000000 | 182.000000 | 184.000000 | 184.000000 | 182.000000 | 175.000000 | 163.000000 | 148.000000 | 136.000000 | 131.000000 | 127.000000 | 124.000000 | 130.000000 | 138.000000 | 142.000000 | 146.000000 | 150.000000 | 154.000000 | 160.00000 | 166.000000 | 173.000000 | 177.00000 | 182.000000 | 183.000000 | 183.000000 | 173.000000 | 176.000000 | 179.000000 | 182.000000 | 184.000000 | 186.000000 | 185.000000 | 182.000000 | 175.000000 | 162.000000 | 145.000000 | 132.000000 | 125.000000 | 124.000000 | 125.000000 | 129.000000 | 137.000000 | 142.000000 | 145.000000 | 146.000000 | 149.000000 | 156.000000 | 164.000000 | 171.000000 | 177.000000 | 183.000000 | 184.000000 | 184.000000 | 174.000000 | 177.000000 | 180.000000 | 183.000000 | 185.000000 | 187.000000 | 187.000000 | 183.000000 | 175.000000 | 161.000000 | 144.000000 | 130.000000 | 123.000000 | 122.000000 | 127.000000 | 132.000000 | 137.000000 | 141.000000 | 144.000000 | 145.000000 | 144.000000 | 151.000000 | 161.000000 | 171.000000 | 179.000000 | 183.000000 | 185.000000 | 185.00000 | 175.000000 | 178.000000 | 181.000000 | 184.000000 | 186.000000 | 188.000000 | 187.000000 | 185.000000 | 176.000000 | 162.000000 | 144.000000 | 131.000000 | 125.000000 | 125.000000 | 128.000000 | 135.000000 | 138.000000 | 143.000000 | 143.000000 | 142.000000 | 142.000000 | 146.000000 | 158.000000 | 170.000000 | 180.000000 | 185.000000 | 186.000000 | 186.000000 | 176.000000 | 179.000000 | 182.000000 | 185.00000 | 187.000000 | 188.000000 | 188.000000 | 185.000000 | 177.000000 | 162.000000 | 146.000000 | 134.000000 | 128.000000 | 128.000000 | 129.000000 | 136.000000 | 141.000000 | 144.000000 | 141.000000 | 139.000000 | 139.000000 | 141.000000 | 157.000000 | 172.000000 | 182.000000 | 185.000000 | 186.000000 | 187.000000 | 175.000000 | 179.000000 | 182.000000 | 185.000000 | 187.000000 | 188.000000 | 189.000000 | 186.000000 | 177.000000 | 164.000000 | 149.000000 | 137.000000 | 133.000000 | 132.000000 | 134.000000 | 138.000000 | 141.000000 | 141.000000 | 138.000000 | 135.000000 | 133.000000 | 140.000000 | 161.000000 | 178.000000 | 185.000000 | 187.000000 | 188.000000 | 187.000000 | 174.000000 | 178.000000 | 182.000000 | 185.000000 | 187.000000 | 188.000000 | 188.000000 | 186.000000 | 178.000000 | 166.000000 | 153.000000 | 142.000000 | 138.000000 | 137.000000 | 140.000000 | 143.000000 | 143.000000 | 139.000000 | 135.000000 | 130.000000 | 130.000000 | 143.000000 | 168.000000 | 182.000000 | 186.000000 | 189.000000 | 189.000000 | 188.000000 | 173.000000 | 176.000000 | 180.000000 | 183.000000 | 186.000000 | 186.000000 | 187.000000 | 185.000000 | 178.000000 | 168.000000 | 157.000000 | 147.000000 | 143.000000 | 141.000000 | 143.000000 | 145.000000 | 142.000000 | 137.000000 | 133.000000 | 128.000000 | 132.000000 | 154.000000 | 176.000000 | 186.000000 | 188.000000 | 189.000000 | 189.000000 | 188.000000 | 171.000000 | 174.000000 | 178.000000 | 180.000000 | 183.000000 | 185.000000 | 184.000000 | 184.000000 | 178.000000 | 168.000000 | 159.000000 | 151.000000 | 145.000000 | 143.000000 | 146.000000 | 145.000000 | 140.000000 | 133.000000 | 129.000000 | 129.000000 | 141.000000 | 164.000000 | 181.000000 | 187.000000 | 188.000000 | 189.000000 | 188.000000 | 187.000000 | 169.000000 | 172.000000 | 175.000000 | 178.000000 | 181.000000 | 182.000000 | 183.000000 | 181.000000 | 177.000000 | 169.000000 | 161.000000 | 153.000000 | 146.000000 | 144.000000 | 145.000000 | 142.000000 | 137.000000 | 131.000000 | 130.000000 | 134.000000 | 152.000000 | 173.000000 | 184.000000 | 187.000000 | 189.000000 | 189.000000 | 188.000000 | 187.000000 | 166.000000 | 170.000000 | 173.000000 | 175.000000 | 177.000000 | 179.000000 | 179.000000 | 178.000000 | 174.000000 | 168.000000 | 161.000000 | 153.000000 | 147.000000 | 143.000000 | 142.000000 | 139.000000 | 134.000000 | 131.000000 | 130.000000 | 138.000000 | 162.000000 | 178.000000 | 186.000000 | 188.000000 | 189.000000 | 188.000000 | 188.000000 | 187.000000 | 162.000000 | 165.000000 | 168.00000 | 170.000000 | 172.000000 | 174.000000 | 174.000000 | 172.000000 | 170.000000 | 166.000000 | 160.000000 | 152.000000 | 145.000000 | 141.000000 | 138.000000 | 135.000000 | 132.000000 | 129.000000 | 131.000000 | 146.000000 | 169.000000 | 181.000000 | 186.000000 | 187.000000 | 188.000000 | 187.000000 | 187.000000 | 186.000000 | 157.000000 | 159.000000 | 161.000000 | 164.000000 | 166.000000 | 167.000000 | 168.000000 | 167.000000 | 165.000000 | 163.000000 | 157.000000 | 150.000000 | 143.000000 | 138.000000 | 135.000000 | 132.000000 | 129.000000 | 127.000000 | 134.000000 | 153.000000 | 173.000000 | 181.000000 | 184.000000 | 187.000000 | 187.000000 | 187.000000 | 186.000000 | 185.000000 | 151.000000 | 152.000000 | 155.000000 | 157.000000 | 160.000000 | 161.000000 | 161.000000 | 159.000000 | 159.000000 | 157.000000 | 153.000000 | 147.000000 | 141.000000 | 138.000000 | 133.000000 | 129.000000 | 127.000000 | 130.000000 | 139.000000 | 161.000000 | 175.000000 | 181.000000 | 184.000000 | 185.000000 | 186.000000 | 185.000000 | 184.000000 | 184.000000 | 145.000000 | 146.000000 | 149.000000 | 151.000000 | 153.000000 | 153.000000 | 154.000000 | 152.000000 | 153.000000 | 152.000000 | 150.000000 | 146.000000 | 141.000000 | 137.000000 | 131.000000 | 128.000000 | 128.000000 | 132.000000 | 144.000000 | 162.000000 | 172.000000 | 180.000000 | 183.000000 | 184.000000 | 184.000000 | 182.000000 | 182.000000 | 182.000000 |
| 75% | 19.000000 | 174.000000 | 176.000000 | 178.000000 | 179.000000 | 181.000000 | 182.000000 | 183.000000 | 184.000000 | 185.000000 | 186.000000 | 186.000000 | 187.000000 | 187.000000 | 187.000000 | 188.000000 | 188.000000 | 188.000000 | 187.000000 | 187.000000 | 187.000000 | 187.000000 | 186.000000 | 186.000000 | 186.000000 | 185.000000 | 184.000000 | 184.000000 | 183.000000 | 176.000000 | 178.000000 | 180.000000 | 181.000000 | 183.000000 | 184.000000 | 185.000000 | 186.000000 | 187.000000 | 187.000000 | 188.000000 | 188.000000 | 188.000000 | 189.000000 | 189.000000 | 189.000000 | 189.000000 | 189.000000 | 188.000000 | 188.000000 | 188.000000 | 188.000000 | 188.000000 | 187.000000 | 187.000000 | 186.000000 | 185.000000 | 185.000000 | 178.000000 | 180.000000 | 182.000000 | 183.000000 | 185.000000 | 186.000000 | 187.000000 | 188.000000 | 189.000000 | 189.000000 | 190.000000 | 190.000000 | 190.000000 | 190.000000 | 190.000000 | 189.000000 | 189.000000 | 189.000000 | 189.000000 | 189.000000 | 189.000000 | 189.000000 | 189.000000 | 189.000000 | 188.000000 | 188.000000 | 187.000000 | 187.000000 | 180.000000 | 182.000000 | 184.000000 | 185.000000 | 187.000000 | 188.000000 | 189.000000 | 190.000000 | 191.000000 | 191.000000 | 192.000000 | 192.000000 | 192.000000 | 192.000000 | 191.000000 | 189.000000 | 189.000000 | 188.000000 | 188.000000 | 188.000000 | 189.000000 | 190.000000 | 190.000000 | 190.000000 | 190.000000 | 189.000000 | 189.000000 | 189.000000 | 182.000000 | 184.000000 | 186.000000 | 187.000000 | 188.000000 | 190.000000 | 191.000000 | 192.000000 | 193.000000 | 193.000000 | 193.000000 | 193.000000 | 193.000000 | 192.000000 | 190.000000 | 189.000000 | 188.000000 | 187.000000 | 186.000000 | 187.000000 | 188.000000 | 189.000000 | 190.000000 | 191.000000 | 191.000000 | 191.000000 | 191.000000 | 190.500000 | 184.000000 | 186.000000 | 187.000000 | 189.000000 | 190.000000 | 191.000000 | 193.000000 | 195.000000 | 195.000000 | 195.000000 | 195.000000 | 195.000000 | 193.000000 | 193.000000 | 191.000000 | 189.000000 | 188.000000 | 186.000000 | 186.000000 | 186.000000 | 187.000000 | 189.000000 | 190.000000 | 191.000000 | 192.000000 | 193.000000 | 193.000000 | 192.000000 | 186.000000 | 188.000000 | 189.000000 | 191.000000 | 192.000000 | 193.000000 | 195.000000 | 197.000000 | 197.000000 | 196.000000 | 196.000000 | 195.000000 | 193.000000 | 192.000000 | 191.000000 | 188.000000 | 187.000000 | 186.000000 | 186.000000 | 186.000000 | 186.000000 | 188.000000 | 190.000000 | 192.000000 | 193.000000 | 194.000000 | 194.000000 | 194.000000 | 188.000000 | 189.000000 | 191.000000 | 193.000000 | 194.000000 | 195.000000 | 197.000000 | 198.000000 | 198.000000 | 197.000000 | 196.000000 | 194.000000 | 192.000000 | 190.000000 | 188.000000 | 187.000000 | 186.000000 | 184.000000 | 185.000000 | 186.000000 | 186.000000 | 188.000000 | 191.000000 | 193.000000 | 194.000000 | 195.000000 | 195.000000 | 195.000000 | 189.000000 | 191.000000 | 193.000000 | 195.000000 | 196.000000 | 197.000000 | 199.000000 | 199.000000 | 199.000000 | 197.000000 | 196.000000 | 193.000000 | 190.000000 | 188.000000 | 185.000000 | 184.000000 | 184.000000 | 183.000000 | 184.000000 | 184.000000 | 186.000000 | 188.000000 | 191.000000 | 194.000000 | 195.000000 | 196.000000 | 197.000000 | 197.000000 | 191.000000 | 193.000000 | 194.000000 | 196.000000 | 198.000000 | 199.000000 | 200.000000 | 200.000000 | 199.000000 | 197.000000 | 194.000000 | 190.000000 | 187.000000 | 184.000000 | 182.000000 | 180.000000 | 180.00000 | 181.000000 | 183.000000 | 184.000000 | 186.000000 | 189.000000 | 193.000000 | 194.000000 | 195.000000 | 197.000000 | 198.000000 | 198.000000 | 192.000000 | 194.000000 | 196.000000 | 198.000000 | 200.000000 | 201.000000 | 202.000000 | 201.000000 | 199.000000 | 196.000000 | 192.000000 | 187.000000 | 183.000000 | 180.000000 | 177.000000 | 176.000000 | 177.000000 | 180.000000 | 183.000000 | 185.000000 | 186.000000 | 189.000000 | 193.000000 | 195.000000 | 197.000000 | 198.000000 | 199.000000 | 199.500000 | 194.000000 | 195.000000 | 197.000000 | 199.000000 | 201.000000 | 202.000000 | 204.000000 | 202.000000 | 200.000000 | 195.000000 | 190.000000 | 184.000000 | 179.000000 | 174.000000 | 171.000000 | 171.000000 | 176.000000 | 181.000000 | 184.000000 | 186.000000 | 188.000000 | 191.000000 | 193.000000 | 195.000000 | 197.000000 | 200.000000 | 201.000000 | 201.000000 | 195.000000 | 197.000000 | 199.000000 | 201.000000 | 202.000000 | 204.000000 | 205.000000 | 204.000000 | 200.000000 | 194.000000 | 187.000000 | 179.000000 | 174.000000 | 170.000000 | 166.000000 | 168.000000 | 176.000000 | 181.000000 | 184.000000 | 186.000000 | 189.000000 | 191.000000 | 194.000000 | 196.000000 | 198.000000 | 200.000000 | 202.000000 | 201.000000 | 196.000000 | 198.000000 | 200.000000 | 202.000000 | 204.000000 | 205.000000 | 206.000000 | 205.000000 | 201.000000 | 194.000000 | 184.000000 | 175.000000 | 169.000000 | 164.000000 | 162.000000 | 168.000000 | 176.000000 | 181.000000 | 183.000000 | 186.000000 | 188.000000 | 191.00000 | 194.000000 | 196.000000 | 198.00000 | 201.000000 | 202.000000 | 202.000000 | 198.000000 | 200.000000 | 201.000000 | 203.000000 | 206.000000 | 207.000000 | 207.000000 | 206.000000 | 202.000000 | 194.000000 | 181.000000 | 171.000000 | 163.000000 | 160.000000 | 163.000000 | 168.000000 | 176.000000 | 181.000000 | 183.000000 | 185.000000 | 186.000000 | 190.000000 | 194.000000 | 196.000000 | 199.000000 | 202.000000 | 203.000000 | 203.000000 | 199.000000 | 201.000000 | 202.000000 | 204.000000 | 207.000000 | 209.000000 | 209.000000 | 208.000000 | 203.000000 | 193.000000 | 180.000000 | 168.000000 | 160.000000 | 161.000000 | 166.000000 | 174.000000 | 178.000000 | 182.000000 | 183.000000 | 183.000000 | 185.000000 | 190.000000 | 193.000000 | 197.000000 | 200.000000 | 203.000000 | 204.000000 | 204.00000 | 200.000000 | 201.000000 | 204.000000 | 206.000000 | 208.000000 | 210.000000 | 210.000000 | 209.000000 | 204.000000 | 193.000000 | 178.000000 | 167.000000 | 162.000000 | 163.000000 | 170.000000 | 177.000000 | 181.000000 | 185.000000 | 182.000000 | 182.000000 | 183.000000 | 188.000000 | 194.000000 | 198.000000 | 202.000000 | 204.000000 | 205.000000 | 205.000000 | 200.000000 | 202.000000 | 205.000000 | 207.00000 | 209.000000 | 210.000000 | 211.000000 | 210.000000 | 205.000000 | 194.000000 | 179.000000 | 169.000000 | 166.000000 | 169.000000 | 174.500000 | 181.000000 | 184.000000 | 184.000000 | 181.000000 | 178.000000 | 180.000000 | 186.000000 | 195.000000 | 199.000000 | 203.000000 | 205.000000 | 206.000000 | 206.000000 | 200.000000 | 202.000000 | 205.000000 | 208.000000 | 209.000000 | 210.000000 | 212.000000 | 210.000000 | 205.000000 | 196.000000 | 183.000000 | 173.000000 | 170.000000 | 174.000000 | 179.500000 | 184.000000 | 184.000000 | 183.000000 | 176.000000 | 174.000000 | 178.000000 | 187.000000 | 196.000000 | 202.000000 | 206.000000 | 206.000000 | 207.000000 | 207.000000 | 200.000000 | 202.000000 | 205.000000 | 208.000000 | 209.000000 | 210.000000 | 212.000000 | 211.000000 | 206.000000 | 198.000000 | 187.000000 | 177.000000 | 174.000000 | 178.000000 | 183.000000 | 186.000000 | 185.000000 | 181.000000 | 174.000000 | 170.000000 | 178.000000 | 191.000000 | 200.000000 | 205.000000 | 207.000000 | 208.000000 | 208.000000 | 207.000000 | 199.000000 | 202.000000 | 205.000000 | 207.000000 | 208.000000 | 209.000000 | 210.000000 | 211.000000 | 206.000000 | 199.000000 | 189.000000 | 180.000000 | 179.000000 | 180.000000 | 184.000000 | 186.000000 | 184.000000 | 177.000000 | 171.000000 | 170.000000 | 184.000000 | 196.000000 | 203.000000 | 207.000000 | 208.000000 | 208.000000 | 208.000000 | 207.000000 | 199.000000 | 201.000000 | 204.000000 | 205.000000 | 207.000000 | 208.000000 | 209.000000 | 210.000000 | 206.000000 | 200.000000 | 192.000000 | 184.000000 | 180.500000 | 180.000000 | 183.000000 | 185.000000 | 182.000000 | 175.000000 | 168.000000 | 178.000000 | 191.000000 | 200.000000 | 206.000000 | 208.000000 | 208.000000 | 208.000000 | 207.000000 | 207.000000 | 198.000000 | 201.000000 | 203.000000 | 204.000000 | 206.000000 | 207.000000 | 208.000000 | 209.000000 | 205.000000 | 199.000000 | 192.000000 | 185.000000 | 181.000000 | 179.000000 | 180.000000 | 181.000000 | 178.000000 | 173.000000 | 174.000000 | 186.000000 | 196.000000 | 203.000000 | 207.000000 | 208.000000 | 209.000000 | 208.000000 | 207.000000 | 206.000000 | 197.000000 | 199.000000 | 201.000000 | 202.000000 | 204.000000 | 205.000000 | 206.000000 | 206.000000 | 203.000000 | 199.000000 | 192.000000 | 185.500000 | 180.000000 | 177.000000 | 177.000000 | 177.000000 | 174.000000 | 174.000000 | 181.000000 | 191.000000 | 201.000000 | 206.000000 | 208.000000 | 208.000000 | 208.000000 | 208.000000 | 207.000000 | 206.000000 | 195.000000 | 197.000000 | 199.00000 | 200.000000 | 202.000000 | 203.000000 | 203.000000 | 202.000000 | 201.000000 | 197.000000 | 192.000000 | 184.000000 | 179.000000 | 174.000000 | 174.000000 | 175.000000 | 174.000000 | 178.000000 | 185.000000 | 195.000000 | 203.000000 | 206.000000 | 208.000000 | 208.000000 | 208.000000 | 208.000000 | 207.000000 | 206.000000 | 193.000000 | 194.000000 | 196.000000 | 197.000000 | 199.000000 | 200.000000 | 199.000000 | 199.000000 | 198.000000 | 195.000000 | 189.000000 | 182.000000 | 177.000000 | 172.000000 | 171.000000 | 172.000000 | 176.000000 | 181.000000 | 190.000000 | 199.000000 | 204.000000 | 206.000000 | 208.000000 | 208.000000 | 208.000000 | 208.000000 | 207.000000 | 206.000000 | 190.000000 | 191.000000 | 193.000000 | 195.000000 | 196.000000 | 196.000000 | 195.000000 | 196.000000 | 195.000000 | 191.000000 | 187.000000 | 181.000000 | 176.000000 | 173.000000 | 172.000000 | 172.000000 | 177.000000 | 186.000000 | 195.000000 | 201.000000 | 205.000000 | 207.000000 | 207.000000 | 208.000000 | 208.000000 | 207.000000 | 206.000000 | 205.000000 | 187.000000 | 187.000000 | 190.000000 | 192.000000 | 193.000000 | 193.000000 | 192.000000 | 193.000000 | 192.000000 | 188.000000 | 185.000000 | 181.000000 | 176.000000 | 174.000000 | 172.000000 | 173.000000 | 180.000000 | 189.000000 | 196.000000 | 202.000000 | 205.000000 | 207.000000 | 208.000000 | 207.000000 | 207.000000 | 206.000000 | 204.000000 | 204.000000 |
| max | 24.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.00000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.00000 | 255.000000 | 255.000000 | 255.00000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.00000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.00000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.00000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 |
# Surface the features that have NaNs
null_counts = Xy_original.isnull().sum()
if null_counts.sum() > 0 :
print(null_counts[null_counts > 0])
print('Total number of NaN in the dataframe:', null_counts.sum())
Total number of NaN in the dataframe: 0
# Standardize the class column to the name of targetVar if required
Xy_original = Xy_original.rename(columns={'label': 'targetVar'})
# Use variable totCol to hold the number of columns in the dataframe
totCol = len(Xy_original.columns)
# Set up variable totAttr for the total number of attribute columns
totAttr = totCol-1
# targetCol variable indicates the column location of the target/class variable
# If the first column, set targetCol to 1. If the last column, set targetCol to totCol
# If (targetCol <> 1) and (targetCol <> totCol), be aware when slicing up the dataframes for visualization
targetCol = 1
# We create attribute-only and target-only datasets (X_original and y_original)
# for various visualization and cleaning/transformation operations
if targetCol == totCol:
X_train_df = Xy_original.iloc[:,0:totAttr]
y_train_df = Xy_original.iloc[:,totAttr]
else:
X_train_df = Xy_original.iloc[:,1:totCol]
y_train_df = Xy_original.iloc[:,0]
print("Xy_original.shape: {} X_train_df.shape: {} y_train_df.shape: {}".format(Xy_original.shape, X_train_df.shape, y_train_df.shape))
Xy_original.shape: (27455, 785) X_train_df.shape: (27455, 784) y_train_df.shape: (27455,)
dataset_path = 'https://dainesanalytics.com/datasets/kaggle-sign-language-mnist/sign_mnist_test.csv'
Xy_test = pd.read_csv(dataset_path, sep=',')
# Take a peek at the dataframe after import
Xy_test.head()
| label | pixel1 | pixel2 | pixel3 | pixel4 | pixel5 | pixel6 | pixel7 | pixel8 | pixel9 | pixel10 | pixel11 | pixel12 | pixel13 | pixel14 | pixel15 | pixel16 | pixel17 | pixel18 | pixel19 | pixel20 | pixel21 | pixel22 | pixel23 | pixel24 | pixel25 | pixel26 | pixel27 | pixel28 | pixel29 | pixel30 | pixel31 | pixel32 | pixel33 | pixel34 | pixel35 | pixel36 | pixel37 | pixel38 | pixel39 | pixel40 | pixel41 | pixel42 | pixel43 | pixel44 | pixel45 | pixel46 | pixel47 | pixel48 | pixel49 | pixel50 | pixel51 | pixel52 | pixel53 | pixel54 | pixel55 | pixel56 | pixel57 | pixel58 | pixel59 | pixel60 | pixel61 | pixel62 | pixel63 | pixel64 | pixel65 | pixel66 | pixel67 | pixel68 | pixel69 | pixel70 | pixel71 | pixel72 | pixel73 | pixel74 | pixel75 | pixel76 | pixel77 | pixel78 | pixel79 | pixel80 | pixel81 | pixel82 | pixel83 | pixel84 | pixel85 | pixel86 | pixel87 | pixel88 | pixel89 | pixel90 | pixel91 | pixel92 | pixel93 | pixel94 | pixel95 | pixel96 | pixel97 | pixel98 | pixel99 | pixel100 | pixel101 | pixel102 | pixel103 | pixel104 | pixel105 | pixel106 | pixel107 | pixel108 | pixel109 | pixel110 | pixel111 | pixel112 | pixel113 | pixel114 | pixel115 | pixel116 | pixel117 | pixel118 | pixel119 | pixel120 | pixel121 | pixel122 | pixel123 | pixel124 | pixel125 | pixel126 | pixel127 | pixel128 | pixel129 | pixel130 | pixel131 | pixel132 | pixel133 | pixel134 | pixel135 | pixel136 | pixel137 | pixel138 | pixel139 | pixel140 | pixel141 | pixel142 | pixel143 | pixel144 | pixel145 | pixel146 | pixel147 | pixel148 | pixel149 | pixel150 | pixel151 | pixel152 | pixel153 | pixel154 | pixel155 | pixel156 | pixel157 | pixel158 | pixel159 | pixel160 | pixel161 | pixel162 | pixel163 | pixel164 | pixel165 | pixel166 | pixel167 | pixel168 | pixel169 | pixel170 | pixel171 | pixel172 | pixel173 | pixel174 | pixel175 | pixel176 | pixel177 | pixel178 | pixel179 | pixel180 | pixel181 | pixel182 | pixel183 | pixel184 | pixel185 | pixel186 | pixel187 | pixel188 | pixel189 | pixel190 | pixel191 | pixel192 | pixel193 | pixel194 | pixel195 | pixel196 | pixel197 | pixel198 | pixel199 | pixel200 | pixel201 | pixel202 | pixel203 | pixel204 | pixel205 | pixel206 | pixel207 | pixel208 | pixel209 | pixel210 | pixel211 | pixel212 | pixel213 | pixel214 | pixel215 | pixel216 | pixel217 | pixel218 | pixel219 | pixel220 | pixel221 | pixel222 | pixel223 | pixel224 | pixel225 | pixel226 | pixel227 | pixel228 | pixel229 | pixel230 | pixel231 | pixel232 | pixel233 | pixel234 | pixel235 | pixel236 | pixel237 | pixel238 | pixel239 | pixel240 | pixel241 | pixel242 | pixel243 | pixel244 | pixel245 | pixel246 | pixel247 | pixel248 | pixel249 | pixel250 | pixel251 | pixel252 | pixel253 | pixel254 | pixel255 | pixel256 | pixel257 | pixel258 | pixel259 | pixel260 | pixel261 | pixel262 | pixel263 | pixel264 | pixel265 | pixel266 | pixel267 | pixel268 | pixel269 | pixel270 | pixel271 | pixel272 | pixel273 | pixel274 | pixel275 | pixel276 | pixel277 | pixel278 | pixel279 | pixel280 | pixel281 | pixel282 | pixel283 | pixel284 | pixel285 | pixel286 | pixel287 | pixel288 | pixel289 | pixel290 | pixel291 | pixel292 | pixel293 | pixel294 | pixel295 | pixel296 | pixel297 | pixel298 | pixel299 | pixel300 | pixel301 | pixel302 | pixel303 | pixel304 | pixel305 | pixel306 | pixel307 | pixel308 | pixel309 | pixel310 | pixel311 | pixel312 | pixel313 | pixel314 | pixel315 | pixel316 | pixel317 | pixel318 | pixel319 | pixel320 | pixel321 | pixel322 | pixel323 | pixel324 | pixel325 | pixel326 | pixel327 | pixel328 | pixel329 | pixel330 | pixel331 | pixel332 | pixel333 | pixel334 | pixel335 | pixel336 | pixel337 | pixel338 | pixel339 | pixel340 | pixel341 | pixel342 | pixel343 | pixel344 | pixel345 | pixel346 | pixel347 | pixel348 | pixel349 | pixel350 | pixel351 | pixel352 | pixel353 | pixel354 | pixel355 | pixel356 | pixel357 | pixel358 | pixel359 | pixel360 | pixel361 | pixel362 | pixel363 | pixel364 | pixel365 | pixel366 | pixel367 | pixel368 | pixel369 | pixel370 | pixel371 | pixel372 | pixel373 | pixel374 | pixel375 | pixel376 | pixel377 | pixel378 | pixel379 | pixel380 | pixel381 | pixel382 | pixel383 | pixel384 | pixel385 | pixel386 | pixel387 | pixel388 | pixel389 | pixel390 | pixel391 | pixel392 | pixel393 | pixel394 | pixel395 | pixel396 | pixel397 | pixel398 | pixel399 | pixel400 | pixel401 | pixel402 | pixel403 | pixel404 | pixel405 | pixel406 | pixel407 | pixel408 | pixel409 | pixel410 | pixel411 | pixel412 | pixel413 | pixel414 | pixel415 | pixel416 | pixel417 | pixel418 | pixel419 | pixel420 | pixel421 | pixel422 | pixel423 | pixel424 | pixel425 | pixel426 | pixel427 | pixel428 | pixel429 | pixel430 | pixel431 | pixel432 | pixel433 | pixel434 | pixel435 | pixel436 | pixel437 | pixel438 | pixel439 | pixel440 | pixel441 | pixel442 | pixel443 | pixel444 | pixel445 | pixel446 | pixel447 | pixel448 | pixel449 | pixel450 | pixel451 | pixel452 | pixel453 | pixel454 | pixel455 | pixel456 | pixel457 | pixel458 | pixel459 | pixel460 | pixel461 | pixel462 | pixel463 | pixel464 | pixel465 | pixel466 | pixel467 | pixel468 | pixel469 | pixel470 | pixel471 | pixel472 | pixel473 | pixel474 | pixel475 | pixel476 | pixel477 | pixel478 | pixel479 | pixel480 | pixel481 | pixel482 | pixel483 | pixel484 | pixel485 | pixel486 | pixel487 | pixel488 | pixel489 | pixel490 | pixel491 | pixel492 | pixel493 | pixel494 | pixel495 | pixel496 | pixel497 | pixel498 | pixel499 | pixel500 | pixel501 | pixel502 | pixel503 | pixel504 | pixel505 | pixel506 | pixel507 | pixel508 | pixel509 | pixel510 | pixel511 | pixel512 | pixel513 | pixel514 | pixel515 | pixel516 | pixel517 | pixel518 | pixel519 | pixel520 | pixel521 | pixel522 | pixel523 | pixel524 | pixel525 | pixel526 | pixel527 | pixel528 | pixel529 | pixel530 | pixel531 | pixel532 | pixel533 | pixel534 | pixel535 | pixel536 | pixel537 | pixel538 | pixel539 | pixel540 | pixel541 | pixel542 | pixel543 | pixel544 | pixel545 | pixel546 | pixel547 | pixel548 | pixel549 | pixel550 | pixel551 | pixel552 | pixel553 | pixel554 | pixel555 | pixel556 | pixel557 | pixel558 | pixel559 | pixel560 | pixel561 | pixel562 | pixel563 | pixel564 | pixel565 | pixel566 | pixel567 | pixel568 | pixel569 | pixel570 | pixel571 | pixel572 | pixel573 | pixel574 | pixel575 | pixel576 | pixel577 | pixel578 | pixel579 | pixel580 | pixel581 | pixel582 | pixel583 | pixel584 | pixel585 | pixel586 | pixel587 | pixel588 | pixel589 | pixel590 | pixel591 | pixel592 | pixel593 | pixel594 | pixel595 | pixel596 | pixel597 | pixel598 | pixel599 | pixel600 | pixel601 | pixel602 | pixel603 | pixel604 | pixel605 | pixel606 | pixel607 | pixel608 | pixel609 | pixel610 | pixel611 | pixel612 | pixel613 | pixel614 | pixel615 | pixel616 | pixel617 | pixel618 | pixel619 | pixel620 | pixel621 | pixel622 | pixel623 | pixel624 | pixel625 | pixel626 | pixel627 | pixel628 | pixel629 | pixel630 | pixel631 | pixel632 | pixel633 | pixel634 | pixel635 | pixel636 | pixel637 | pixel638 | pixel639 | pixel640 | pixel641 | pixel642 | pixel643 | pixel644 | pixel645 | pixel646 | pixel647 | pixel648 | pixel649 | pixel650 | pixel651 | pixel652 | pixel653 | pixel654 | pixel655 | pixel656 | pixel657 | pixel658 | pixel659 | pixel660 | pixel661 | pixel662 | pixel663 | pixel664 | pixel665 | pixel666 | pixel667 | pixel668 | pixel669 | pixel670 | pixel671 | pixel672 | pixel673 | pixel674 | pixel675 | pixel676 | pixel677 | pixel678 | pixel679 | pixel680 | pixel681 | pixel682 | pixel683 | pixel684 | pixel685 | pixel686 | pixel687 | pixel688 | pixel689 | pixel690 | pixel691 | pixel692 | pixel693 | pixel694 | pixel695 | pixel696 | pixel697 | pixel698 | pixel699 | pixel700 | pixel701 | pixel702 | pixel703 | pixel704 | pixel705 | pixel706 | pixel707 | pixel708 | pixel709 | pixel710 | pixel711 | pixel712 | pixel713 | pixel714 | pixel715 | pixel716 | pixel717 | pixel718 | pixel719 | pixel720 | pixel721 | pixel722 | pixel723 | pixel724 | pixel725 | pixel726 | pixel727 | pixel728 | pixel729 | pixel730 | pixel731 | pixel732 | pixel733 | pixel734 | pixel735 | pixel736 | pixel737 | pixel738 | pixel739 | pixel740 | pixel741 | pixel742 | pixel743 | pixel744 | pixel745 | pixel746 | pixel747 | pixel748 | pixel749 | pixel750 | pixel751 | pixel752 | pixel753 | pixel754 | pixel755 | pixel756 | pixel757 | pixel758 | pixel759 | pixel760 | pixel761 | pixel762 | pixel763 | pixel764 | pixel765 | pixel766 | pixel767 | pixel768 | pixel769 | pixel770 | pixel771 | pixel772 | pixel773 | pixel774 | pixel775 | pixel776 | pixel777 | pixel778 | pixel779 | pixel780 | pixel781 | pixel782 | pixel783 | pixel784 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 6 | 149 | 149 | 150 | 150 | 150 | 151 | 151 | 150 | 151 | 152 | 152 | 152 | 152 | 152 | 153 | 153 | 151 | 152 | 152 | 153 | 152 | 152 | 151 | 151 | 150 | 150 | 150 | 149 | 150 | 150 | 150 | 152 | 152 | 151 | 152 | 152 | 152 | 152 | 152 | 153 | 154 | 153 | 154 | 154 | 153 | 154 | 153 | 154 | 153 | 153 | 152 | 152 | 152 | 151 | 150 | 151 | 150 | 151 | 151 | 152 | 152 | 152 | 153 | 153 | 152 | 152 | 152 | 153 | 154 | 154 | 155 | 155 | 154 | 154 | 155 | 155 | 155 | 155 | 154 | 153 | 153 | 151 | 151 | 152 | 150 | 151 | 151 | 152 | 152 | 152 | 154 | 154 | 154 | 154 | 154 | 153 | 154 | 155 | 156 | 157 | 157 | 156 | 155 | 156 | 155 | 154 | 154 | 155 | 152 | 154 | 153 | 153 | 151 | 152 | 152 | 152 | 154 | 154 | 154 | 154 | 154 | 155 | 157 | 156 | 156 | 156 | 154 | 150 | 146 | 147 | 146 | 147 | 143 | 137 | 126 | 126 | 142 | 139 | 152 | 154 | 152 | 153 | 153 | 154 | 154 | 155 | 154 | 155 | 155 | 154 | 153 | 150 | 144 | 143 | 145 | 139 | 142 | 144 | 157 | 157 | 147 | 139 | 128 | 119 | 130 | 113 | 147 | 156 | 151 | 153 | 153 | 155 | 155 | 156 | 155 | 152 | 145 | 139 | 141 | 141 | 141 | 153 | 153 | 143 | 135 | 137 | 139 | 133 | 121 | 107 | 101 | 104 | 110 | 127 | 157 | 156 | 151 | 152 | 153 | 155 | 155 | 154 | 151 | 146 | 139 | 131 | 130 | 134 | 137 | 132 | 125 | 111 | 101 | 94 | 95 | 105 | 113 | 122 | 133 | 145 | 153 | 157 | 156 | 156 | 152 | 152 | 154 | 152 | 151 | 150 | 149 | 149 | 139 | 122 | 104 | 98 | 92 | 82 | 81 | 81 | 85 | 114 | 145 | 157 | 160 | 162 | 161 | 159 | 157 | 156 | 156 | 156 | 151 | 151 | 150 | 146 | 145 | 147 | 148 | 147 | 145 | 132 | 97 | 71 | 62 | 66 | 88 | 116 | 145 | 162 | 160 | 159 | 157 | 155 | 156 | 157 | 157 | 156 | 155 | 155 | 151 | 145 | 144 | 145 | 147 | 145 | 147 | 150 | 150 | 124 | 92 | 68 | 63 | 67 | 86 | 159 | 163 | 155 | 158 | 157 | 156 | 156 | 157 | 156 | 156 | 156 | 155 | 154 | 143 | 144 | 145 | 145 | 143 | 147 | 152 | 152 | 128 | 90 | 79 | 68 | 64 | 70 | 67 | 84 | 147 | 164 | 157 | 158 | 157 | 157 | 157 | 156 | 157 | 156 | 156 | 155 | 145 | 146 | 143 | 145 | 145 | 150 | 149 | 149 | 139 | 118 | 85 | 62 | 62 | 75 | 73 | 62 | 67 | 140 | 164 | 157 | 158 | 158 | 158 | 158 | 157 | 157 | 156 | 156 | 150 | 147 | 144 | 147 | 149 | 148 | 149 | 158 | 158 | 136 | 94 | 63 | 58 | 69 | 85 | 82 | 67 | 70 | 156 | 160 | 159 | 160 | 159 | 158 | 157 | 156 | 156 | 156 | 147 | 148 | 147 | 145 | 148 | 152 | 151 | 160 | 153 | 119 | 86 | 66 | 64 | 63 | 69 | 75 | 78 | 57 | 130 | 165 | 158 | 159 | 158 | 159 | 158 | 157 | 157 | 157 | 149 | 148 | 146 | 145 | 147 | 149 | 146 | 151 | 144 | 110 | 78 | 65 | 66 | 66 | 58 | 59 | 64 | 79 | 150 | 165 | 162 | 162 | 162 | 162 | 161 | 161 | 158 | 156 | 151 | 146 | 143 | 141 | 138 | 140 | 142 | 146 | 144 | 121 | 84 | 56 | 62 | 70 | 71 | 68 | 57 | 117 | 144 | 144 | 147 | 149 | 152 | 150 | 146 | 146 | 154 | 160 | 147 | 144 | 143 | 142 | 140 | 142 | 146 | 151 | 154 | 131 | 85 | 59 | 51 | 60 | 85 | 69 | 64 | 76 | 75 | 79 | 81 | 79 | 76 | 83 | 112 | 141 | 163 | 163 | 144 | 148 | 147 | 145 | 145 | 148 | 150 | 155 | 151 | 119 | 74 | 62 | 63 | 55 | 62 | 72 | 73 | 77 | 74 | 73 | 68 | 88 | 113 | 138 | 162 | 162 | 168 | 168 | 146 | 146 | 142 | 141 | 141 | 138 | 134 | 142 | 124 | 96 | 75 | 67 | 65 | 63 | 62 | 78 | 87 | 76 | 84 | 96 | 126 | 162 | 172 | 155 | 144 | 149 | 151 | 161 | 142 | 136 | 132 | 134 | 127 | 119 | 118 | 119 | 103 | 87 | 77 | 73 | 70 | 62 | 64 | 72 | 93 | 134 | 155 | 160 | 166 | 156 | 150 | 151 | 143 | 136 | 145 | 149 | 130 | 132 | 127 | 120 | 114 | 110 | 109 | 105 | 91 | 77 | 74 | 75 | 74 | 65 | 73 | 113 | 166 | 177 | 170 | 161 | 152 | 141 | 134 | 136 | 140 | 133 | 127 | 130 | 113 | 116 | 115 | 106 | 101 | 95 | 86 | 84 | 85 | 77 | 78 | 74 | 76 | 103 | 152 | 179 | 170 | 157 | 155 | 151 | 140 | 129 | 126 | 126 | 133 | 130 | 122 | 125 | 81 | 86 | 85 | 83 | 76 | 72 | 73 | 76 | 77 | 79 | 71 | 101 | 151 | 178 | 177 | 170 | 161 | 152 | 147 | 151 | 133 | 115 | 121 | 121 | 124 | 126 | 122 | 122 | 61 | 61 | 67 | 69 | 70 | 75 | 78 | 78 | 81 | 68 | 113 | 165 | 174 | 169 | 162 | 157 | 149 | 148 | 148 | 148 | 126 | 100 | 113 | 117 | 113 | 122 | 118 | 115 | 69 | 69 | 77 | 78 | 75 | 76 | 78 | 79 | 67 | 120 | 173 | 157 | 159 | 148 | 155 | 150 | 138 | 143 | 148 | 149 | 123 | 91 | 101 | 111 | 111 | 116 | 113 | 118 | 74 | 75 | 76 | 75 | 75 | 76 | 75 | 68 | 124 | 172 | 152 | 146 | 146 | 146 | 152 | 142 | 131 | 134 | 144 | 147 | 125 | 87 | 87 | 103 | 107 | 110 | 116 | 113 | 75 | 74 | 74 | 74 | 76 | 74 | 82 | 134 | 168 | 155 | 146 | 137 | 145 | 146 | 149 | 135 | 124 | 125 | 138 | 148 | 127 | 89 | 82 | 96 | 106 | 112 | 120 | 107 |
| 1 | 5 | 126 | 128 | 131 | 132 | 133 | 134 | 135 | 135 | 136 | 138 | 137 | 137 | 138 | 138 | 139 | 137 | 142 | 140 | 138 | 139 | 137 | 137 | 136 | 135 | 134 | 133 | 134 | 132 | 129 | 132 | 134 | 135 | 135 | 137 | 139 | 139 | 139 | 140 | 141 | 141 | 142 | 143 | 142 | 142 | 116 | 138 | 141 | 140 | 141 | 140 | 139 | 138 | 137 | 136 | 136 | 134 | 133 | 135 | 138 | 139 | 139 | 141 | 142 | 143 | 142 | 143 | 145 | 145 | 143 | 145 | 145 | 158 | 94 | 118 | 151 | 143 | 144 | 144 | 142 | 141 | 141 | 140 | 139 | 138 | 137 | 139 | 142 | 142 | 142 | 144 | 146 | 146 | 146 | 147 | 147 | 147 | 148 | 117 | 128 | 168 | 101 | 98 | 157 | 146 | 147 | 146 | 146 | 145 | 144 | 143 | 142 | 141 | 140 | 142 | 145 | 146 | 147 | 148 | 149 | 149 | 149 | 151 | 151 | 149 | 161 | 114 | 99 | 174 | 99 | 84 | 162 | 149 | 151 | 149 | 148 | 147 | 146 | 146 | 145 | 144 | 143 | 145 | 149 | 150 | 150 | 151 | 153 | 153 | 154 | 153 | 154 | 152 | 167 | 126 | 88 | 169 | 99 | 87 | 164 | 152 | 153 | 152 | 151 | 150 | 149 | 148 | 148 | 147 | 145 | 147 | 151 | 152 | 153 | 155 | 155 | 155 | 151 | 154 | 158 | 155 | 170 | 130 | 79 | 166 | 111 | 93 | 166 | 156 | 157 | 156 | 155 | 153 | 152 | 152 | 152 | 150 | 149 | 150 | 153 | 155 | 155 | 158 | 157 | 163 | 129 | 120 | 166 | 156 | 171 | 140 | 82 | 162 | 102 | 97 | 168 | 158 | 160 | 158 | 162 | 160 | 154 | 154 | 154 | 152 | 151 | 152 | 156 | 158 | 159 | 159 | 158 | 164 | 139 | 91 | 165 | 159 | 174 | 144 | 71 | 156 | 96 | 100 | 171 | 161 | 161 | 158 | 128 | 145 | 162 | 156 | 155 | 155 | 152 | 155 | 159 | 160 | 161 | 161 | 160 | 168 | 158 | 76 | 159 | 164 | 172 | 142 | 63 | 155 | 117 | 100 | 174 | 159 | 164 | 164 | 126 | 103 | 162 | 161 | 158 | 157 | 153 | 157 | 160 | 162 | 162 | 162 | 164 | 167 | 158 | 78 | 158 | 167 | 167 | 156 | 73 | 133 | 129 | 102 | 172 | 157 | 148 | 130 | 156 | 132 | 129 | 163 | 161 | 159 | 157 | 159 | 162 | 164 | 164 | 165 | 166 | 167 | 173 | 89 | 139 | 172 | 162 | 163 | 79 | 98 | 132 | 111 | 170 | 160 | 142 | 54 | 125 | 150 | 102 | 150 | 167 | 162 | 159 | 161 | 166 | 165 | 167 | 167 | 167 | 168 | 178 | 118 | 112 | 175 | 164 | 167 | 82 | 91 | 129 | 110 | 160 | 156 | 130 | 96 | 157 | 130 | 106 | 169 | 165 | 164 | 159 | 162 | 166 | 167 | 168 | 168 | 170 | 169 | 164 | 168 | 132 | 141 | 162 | 153 | 103 | 113 | 117 | 96 | 133 | 143 | 107 | 147 | 172 | 99 | 139 | 174 | 165 | 166 | 161 | 164 | 167 | 170 | 171 | 171 | 170 | 173 | 160 | 173 | 162 | 129 | 132 | 132 | 109 | 109 | 108 | 99 | 135 | 142 | 111 | 163 | 154 | 77 | 156 | 172 | 167 | 167 | 165 | 167 | 168 | 171 | 172 | 173 | 173 | 174 | 169 | 170 | 182 | 150 | 125 | 124 | 100 | 106 | 103 | 102 | 130 | 138 | 124 | 178 | 130 | 64 | 168 | 172 | 170 | 169 | 165 | 168 | 170 | 171 | 172 | 174 | 175 | 174 | 175 | 172 | 195 | 170 | 114 | 110 | 94 | 89 | 98 | 105 | 127 | 134 | 124 | 182 | 126 | 80 | 180 | 171 | 171 | 171 | 166 | 169 | 171 | 172 | 173 | 174 | 175 | 176 | 177 | 174 | 197 | 179 | 119 | 86 | 87 | 81 | 94 | 118 | 136 | 123 | 116 | 177 | 127 | 94 | 183 | 172 | 173 | 173 | 169 | 172 | 172 | 174 | 175 | 174 | 177 | 178 | 178 | 175 | 192 | 176 | 126 | 87 | 86 | 82 | 109 | 130 | 147 | 159 | 128 | 164 | 128 | 100 | 184 | 174 | 173 | 173 | 169 | 172 | 173 | 173 | 176 | 178 | 179 | 178 | 181 | 175 | 189 | 171 | 126 | 89 | 80 | 90 | 121 | 137 | 164 | 175 | 141 | 140 | 108 | 95 | 184 | 176 | 175 | 173 | 171 | 173 | 174 | 175 | 177 | 179 | 179 | 179 | 181 | 174 | 189 | 171 | 134 | 91 | 80 | 98 | 134 | 159 | 164 | 167 | 153 | 114 | 73 | 82 | 185 | 176 | 177 | 177 | 172 | 173 | 174 | 177 | 178 | 179 | 180 | 180 | 183 | 174 | 186 | 172 | 138 | 93 | 82 | 97 | 143 | 172 | 169 | 160 | 132 | 89 | 44 | 108 | 189 | 176 | 178 | 178 | 171 | 173 | 177 | 178 | 179 | 180 | 181 | 182 | 185 | 178 | 179 | 170 | 137 | 95 | 88 | 90 | 152 | 180 | 167 | 141 | 112 | 65 | 64 | 176 | 183 | 179 | 179 | 178 | 173 | 174 | 178 | 179 | 179 | 180 | 182 | 183 | 186 | 175 | 165 | 168 | 137 | 100 | 96 | 88 | 149 | 168 | 147 | 122 | 92 | 50 | 144 | 193 | 181 | 181 | 180 | 179 | 173 | 174 | 177 | 179 | 180 | 180 | 183 | 182 | 187 | 177 | 158 | 161 | 130 | 111 | 101 | 91 | 136 | 150 | 135 | 112 | 62 | 87 | 192 | 183 | 185 | 183 | 181 | 180 | 173 | 174 | 177 | 178 | 179 | 179 | 181 | 182 | 184 | 179 | 156 | 151 | 124 | 116 | 96 | 88 | 128 | 138 | 126 | 81 | 49 | 164 | 190 | 184 | 185 | 184 | 182 | 181 | 172 | 174 | 177 | 178 | 178 | 178 | 180 | 182 | 184 | 177 | 160 | 154 | 128 | 114 | 97 | 78 | 114 | 112 | 89 | 48 | 133 | 194 | 182 | 185 | 184 | 184 | 182 | 181 | 172 | 174 | 177 | 178 | 178 | 179 | 181 | 183 | 187 | 175 | 165 | 154 | 118 | 107 | 100 | 75 | 96 | 83 | 47 | 104 | 194 | 183 | 186 | 184 | 184 | 184 | 182 | 180 |
| 2 | 10 | 85 | 88 | 92 | 96 | 105 | 123 | 135 | 143 | 147 | 152 | 157 | 163 | 168 | 171 | 182 | 172 | 175 | 185 | 183 | 184 | 185 | 185 | 185 | 183 | 183 | 182 | 181 | 178 | 86 | 88 | 93 | 96 | 108 | 125 | 137 | 145 | 149 | 154 | 160 | 166 | 170 | 174 | 215 | 154 | 142 | 190 | 184 | 184 | 186 | 187 | 185 | 184 | 183 | 183 | 181 | 179 | 86 | 89 | 93 | 98 | 110 | 127 | 138 | 147 | 150 | 155 | 162 | 169 | 171 | 183 | 214 | 162 | 123 | 187 | 188 | 187 | 189 | 188 | 187 | 186 | 185 | 185 | 183 | 181 | 88 | 91 | 95 | 99 | 111 | 128 | 141 | 149 | 152 | 157 | 165 | 171 | 175 | 184 | 215 | 182 | 116 | 179 | 193 | 190 | 191 | 190 | 189 | 186 | 187 | 186 | 186 | 183 | 88 | 92 | 96 | 99 | 113 | 130 | 144 | 151 | 154 | 160 | 167 | 173 | 180 | 181 | 215 | 206 | 128 | 169 | 198 | 192 | 192 | 192 | 191 | 191 | 191 | 188 | 187 | 186 | 89 | 93 | 98 | 101 | 116 | 133 | 145 | 152 | 155 | 162 | 169 | 175 | 181 | 182 | 226 | 217 | 138 | 157 | 203 | 193 | 194 | 195 | 193 | 193 | 193 | 191 | 189 | 187 | 91 | 94 | 99 | 102 | 118 | 135 | 147 | 154 | 157 | 164 | 172 | 179 | 185 | 183 | 225 | 213 | 148 | 146 | 205 | 196 | 197 | 198 | 196 | 196 | 195 | 193 | 191 | 190 | 91 | 95 | 100 | 101 | 118 | 136 | 149 | 156 | 159 | 167 | 174 | 180 | 186 | 184 | 228 | 223 | 148 | 128 | 205 | 199 | 200 | 200 | 199 | 197 | 196 | 196 | 193 | 193 | 91 | 94 | 99 | 102 | 120 | 139 | 152 | 157 | 161 | 167 | 176 | 184 | 187 | 186 | 232 | 232 | 161 | 112 | 201 | 202 | 201 | 201 | 200 | 199 | 199 | 198 | 195 | 195 | 92 | 95 | 100 | 104 | 123 | 138 | 152 | 163 | 165 | 177 | 209 | 150 | 170 | 194 | 223 | 211 | 162 | 101 | 187 | 208 | 203 | 203 | 203 | 202 | 201 | 200 | 198 | 198 | 92 | 97 | 101 | 106 | 120 | 159 | 193 | 150 | 116 | 161 | 193 | 103 | 127 | 204 | 214 | 191 | 148 | 91 | 168 | 214 | 205 | 206 | 205 | 203 | 203 | 203 | 201 | 200 | 93 | 97 | 101 | 108 | 119 | 176 | 191 | 111 | 86 | 99 | 141 | 104 | 66 | 164 | 205 | 167 | 157 | 95 | 160 | 218 | 206 | 207 | 207 | 205 | 204 | 204 | 202 | 202 | 93 | 98 | 100 | 108 | 126 | 150 | 151 | 113 | 100 | 90 | 128 | 113 | 71 | 72 | 128 | 133 | 166 | 118 | 136 | 220 | 207 | 208 | 208 | 208 | 206 | 205 | 205 | 203 | 94 | 98 | 101 | 109 | 128 | 145 | 159 | 161 | 154 | 142 | 144 | 119 | 101 | 70 | 72 | 93 | 130 | 124 | 105 | 209 | 211 | 210 | 210 | 209 | 208 | 208 | 207 | 205 | 95 | 98 | 101 | 109 | 129 | 147 | 162 | 169 | 181 | 186 | 132 | 138 | 120 | 111 | 85 | 75 | 100 | 108 | 85 | 190 | 220 | 213 | 212 | 210 | 210 | 210 | 208 | 206 | 95 | 98 | 102 | 110 | 129 | 148 | 164 | 167 | 198 | 181 | 101 | 120 | 165 | 146 | 125 | 94 | 83 | 93 | 75 | 179 | 224 | 215 | 215 | 214 | 213 | 211 | 209 | 207 | 93 | 97 | 102 | 112 | 132 | 149 | 166 | 173 | 189 | 195 | 158 | 91 | 127 | 176 | 152 | 132 | 101 | 80 | 69 | 156 | 228 | 215 | 216 | 216 | 214 | 212 | 210 | 208 | 95 | 99 | 100 | 110 | 135 | 147 | 186 | 190 | 153 | 207 | 171 | 108 | 110 | 222 | 169 | 152 | 128 | 79 | 61 | 108 | 226 | 217 | 218 | 218 | 216 | 215 | 213 | 212 | 96 | 99 | 101 | 111 | 134 | 146 | 200 | 221 | 149 | 156 | 161 | 113 | 167 | 247 | 188 | 160 | 139 | 90 | 63 | 92 | 223 | 221 | 220 | 219 | 219 | 217 | 216 | 214 | 95 | 99 | 101 | 112 | 135 | 150 | 187 | 222 | 172 | 113 | 168 | 151 | 173 | 247 | 184 | 153 | 140 | 103 | 67 | 90 | 226 | 223 | 222 | 222 | 220 | 218 | 217 | 216 | 96 | 98 | 101 | 113 | 136 | 155 | 168 | 194 | 184 | 123 | 170 | 201 | 176 | 229 | 196 | 157 | 133 | 107 | 74 | 88 | 225 | 224 | 224 | 224 | 222 | 220 | 218 | 217 | 95 | 98 | 101 | 115 | 137 | 155 | 167 | 183 | 185 | 125 | 169 | 250 | 154 | 197 | 201 | 172 | 130 | 102 | 81 | 86 | 224 | 226 | 225 | 224 | 224 | 222 | 220 | 218 | 94 | 97 | 100 | 115 | 137 | 156 | 171 | 173 | 198 | 163 | 190 | 173 | 100 | 162 | 200 | 161 | 125 | 98 | 80 | 95 | 228 | 227 | 226 | 225 | 224 | 223 | 221 | 219 | 96 | 99 | 100 | 114 | 138 | 157 | 173 | 174 | 190 | 225 | 194 | 116 | 125 | 184 | 183 | 150 | 117 | 94 | 77 | 112 | 236 | 226 | 229 | 227 | 225 | 224 | 221 | 220 | 93 | 97 | 99 | 115 | 138 | 157 | 170 | 175 | 177 | 211 | 222 | 197 | 172 | 191 | 163 | 135 | 110 | 97 | 76 | 132 | 239 | 224 | 227 | 227 | 225 | 224 | 223 | 222 | 91 | 94 | 97 | 114 | 137 | 156 | 170 | 172 | 180 | 199 | 217 | 200 | 157 | 160 | 147 | 122 | 112 | 107 | 80 | 145 | 239 | 225 | 227 | 227 | 225 | 224 | 222 | 221 | 90 | 92 | 96 | 114 | 138 | 155 | 171 | 174 | 185 | 90 | 179 | 207 | 143 | 164 | 167 | 163 | 145 | 123 | 78 | 162 | 239 | 227 | 229 | 226 | 226 | 225 | 224 | 222 | 89 | 91 | 94 | 111 | 136 | 154 | 167 | 184 | 125 | 3 | 166 | 225 | 195 | 188 | 172 | 185 | 161 | 122 | 68 | 166 | 242 | 227 | 230 | 227 | 226 | 225 | 224 | 222 |
| 3 | 0 | 203 | 205 | 207 | 206 | 207 | 209 | 210 | 209 | 210 | 209 | 208 | 207 | 207 | 209 | 208 | 210 | 210 | 207 | 209 | 209 | 208 | 209 | 210 | 209 | 207 | 208 | 209 | 207 | 206 | 208 | 209 | 208 | 208 | 210 | 211 | 210 | 211 | 209 | 209 | 210 | 211 | 211 | 209 | 208 | 211 | 215 | 210 | 212 | 212 | 211 | 211 | 210 | 210 | 211 | 210 | 210 | 209 | 209 | 210 | 211 | 210 | 210 | 212 | 212 | 211 | 212 | 210 | 210 | 212 | 211 | 223 | 208 | 162 | 176 | 219 | 204 | 206 | 217 | 216 | 214 | 212 | 209 | 211 | 212 | 209 | 210 | 211 | 213 | 215 | 213 | 211 | 213 | 213 | 211 | 216 | 216 | 179 | 167 | 228 | 202 | 161 | 129 | 132 | 155 | 141 | 187 | 210 | 159 | 197 | 217 | 211 | 211 | 210 | 211 | 211 | 213 | 214 | 213 | 213 | 214 | 215 | 216 | 245 | 228 | 181 | 138 | 184 | 210 | 175 | 160 | 76 | 133 | 143 | 118 | 180 | 126 | 170 | 225 | 211 | 214 | 210 | 212 | 213 | 213 | 213 | 215 | 213 | 221 | 198 | 181 | 245 | 225 | 192 | 156 | 176 | 234 | 181 | 168 | 95 | 158 | 159 | 121 | 145 | 134 | 161 | 228 | 214 | 216 | 211 | 213 | 214 | 215 | 215 | 215 | 218 | 252 | 199 | 142 | 192 | 244 | 200 | 166 | 160 | 249 | 199 | 175 | 102 | 176 | 164 | 113 | 111 | 137 | 131 | 232 | 213 | 217 | 213 | 214 | 214 | 215 | 217 | 211 | 235 | 246 | 207 | 174 | 164 | 250 | 211 | 166 | 144 | 248 | 204 | 171 | 96 | 179 | 161 | 98 | 94 | 152 | 85 | 209 | 221 | 217 | 214 | 216 | 216 | 216 | 214 | 233 | 229 | 227 | 222 | 190 | 140 | 226 | 220 | 160 | 123 | 239 | 206 | 155 | 77 | 138 | 144 | 88 | 82 | 161 | 76 | 141 | 233 | 216 | 215 | 216 | 217 | 216 | 217 | 237 | 194 | 217 | 235 | 197 | 131 | 193 | 228 | 158 | 100 | 198 | 194 | 137 | 69 | 105 | 127 | 78 | 84 | 166 | 89 | 111 | 231 | 218 | 217 | 217 | 219 | 214 | 230 | 230 | 180 | 192 | 229 | 195 | 129 | 156 | 217 | 151 | 87 | 153 | 177 | 128 | 78 | 88 | 114 | 64 | 102 | 178 | 106 | 115 | 233 | 220 | 217 | 218 | 218 | 216 | 230 | 223 | 188 | 176 | 204 | 173 | 118 | 121 | 191 | 141 | 81 | 110 | 168 | 123 | 74 | 88 | 117 | 74 | 157 | 195 | 120 | 119 | 234 | 222 | 213 | 216 | 215 | 221 | 232 | 209 | 174 | 140 | 183 | 167 | 113 | 96 | 178 | 139 | 71 | 82 | 166 | 122 | 64 | 111 | 127 | 97 | 196 | 189 | 122 | 128 | 235 | 221 | 213 | 215 | 215 | 215 | 246 | 229 | 187 | 129 | 145 | 170 | 101 | 83 | 169 | 144 | 70 | 75 | 173 | 120 | 130 | 197 | 163 | 118 | 184 | 196 | 122 | 138 | 234 | 220 | 215 | 217 | 216 | 217 | 244 | 231 | 192 | 146 | 100 | 141 | 131 | 79 | 171 | 165 | 82 | 85 | 170 | 173 | 196 | 194 | 173 | 137 | 132 | 180 | 112 | 145 | 236 | 221 | 217 | 218 | 217 | 221 | 248 | 235 | 206 | 169 | 123 | 109 | 140 | 103 | 164 | 178 | 148 | 189 | 206 | 203 | 191 | 177 | 154 | 137 | 114 | 117 | 91 | 138 | 238 | 220 | 218 | 219 | 219 | 221 | 249 | 243 | 214 | 182 | 164 | 151 | 142 | 164 | 185 | 198 | 218 | 220 | 218 | 207 | 189 | 170 | 143 | 121 | 113 | 92 | 73 | 131 | 239 | 220 | 217 | 218 | 217 | 222 | 244 | 243 | 223 | 200 | 185 | 173 | 160 | 160 | 187 | 205 | 218 | 217 | 218 | 209 | 183 | 164 | 142 | 114 | 99 | 93 | 66 | 159 | 238 | 222 | 216 | 216 | 217 | 221 | 238 | 238 | 231 | 215 | 204 | 182 | 170 | 165 | 178 | 201 | 215 | 216 | 211 | 202 | 177 | 157 | 136 | 110 | 96 | 76 | 113 | 222 | 230 | 227 | 222 | 223 | 224 | 216 | 233 | 239 | 240 | 229 | 210 | 187 | 171 | 168 | 180 | 207 | 218 | 218 | 203 | 194 | 174 | 153 | 129 | 107 | 80 | 97 | 213 | 227 | 225 | 226 | 175 | 180 | 188 | 178 | 214 | 240 | 245 | 233 | 210 | 191 | 174 | 167 | 179 | 211 | 220 | 211 | 196 | 186 | 168 | 143 | 118 | 96 | 78 | 196 | 239 | 230 | 234 | 233 | 94 | 96 | 103 | 85 | 151 | 244 | 234 | 226 | 211 | 195 | 181 | 165 | 178 | 207 | 207 | 192 | 178 | 168 | 153 | 129 | 107 | 86 | 104 | 177 | 178 | 182 | 178 | 177 | 103 | 103 | 104 | 91 | 114 | 239 | 220 | 212 | 211 | 202 | 188 | 164 | 173 | 203 | 198 | 178 | 156 | 140 | 127 | 115 | 95 | 92 | 99 | 95 | 90 | 86 | 113 | 156 | 101 | 103 | 105 | 99 | 106 | 230 | 209 | 204 | 214 | 204 | 185 | 164 | 167 | 201 | 194 | 168 | 131 | 109 | 103 | 93 | 93 | 104 | 98 | 97 | 131 | 187 | 234 | 233 | 100 | 101 | 100 | 102 | 104 | 222 | 207 | 205 | 215 | 199 | 183 | 172 | 164 | 193 | 187 | 153 | 107 | 91 | 86 | 93 | 102 | 108 | 141 | 195 | 241 | 242 | 226 | 207 | 100 | 101 | 100 | 104 | 98 | 222 | 224 | 209 | 212 | 193 | 178 | 175 | 159 | 171 | 173 | 135 | 92 | 85 | 85 | 92 | 120 | 208 | 252 | 255 | 246 | 237 | 229 | 204 | 101 | 100 | 100 | 102 | 93 | 220 | 233 | 219 | 210 | 191 | 179 | 182 | 155 | 159 | 164 | 123 | 85 | 80 | 84 | 151 | 238 | 255 | 255 | 250 | 237 | 245 | 250 | 232 | 103 | 101 | 102 | 103 | 95 | 208 | 231 | 227 | 209 | 190 | 179 | 182 | 152 | 150 | 159 | 119 | 83 | 63 | 154 | 248 | 247 | 248 | 253 | 236 | 230 | 240 | 253 | 255 |
| 4 | 3 | 188 | 191 | 193 | 195 | 199 | 201 | 202 | 203 | 203 | 203 | 204 | 204 | 204 | 203 | 202 | 198 | 216 | 217 | 135 | 181 | 200 | 195 | 194 | 193 | 190 | 189 | 187 | 185 | 190 | 194 | 196 | 197 | 200 | 202 | 204 | 206 | 207 | 207 | 206 | 205 | 206 | 206 | 205 | 200 | 220 | 206 | 122 | 155 | 207 | 196 | 195 | 195 | 193 | 191 | 188 | 187 | 192 | 195 | 198 | 199 | 203 | 205 | 206 | 208 | 209 | 209 | 209 | 208 | 208 | 207 | 207 | 202 | 219 | 195 | 123 | 146 | 209 | 197 | 197 | 197 | 195 | 191 | 190 | 188 | 193 | 197 | 200 | 201 | 204 | 207 | 209 | 210 | 211 | 211 | 211 | 210 | 210 | 209 | 208 | 205 | 219 | 197 | 123 | 142 | 209 | 199 | 200 | 197 | 195 | 194 | 192 | 190 | 196 | 199 | 202 | 204 | 206 | 209 | 211 | 212 | 213 | 213 | 213 | 212 | 212 | 212 | 210 | 206 | 223 | 221 | 143 | 135 | 207 | 202 | 201 | 200 | 197 | 196 | 194 | 191 | 199 | 201 | 203 | 207 | 209 | 211 | 213 | 215 | 216 | 216 | 216 | 216 | 215 | 213 | 212 | 207 | 229 | 225 | 143 | 127 | 206 | 203 | 202 | 201 | 198 | 196 | 195 | 193 | 199 | 203 | 206 | 209 | 212 | 214 | 216 | 217 | 218 | 218 | 218 | 217 | 216 | 216 | 215 | 208 | 234 | 215 | 133 | 125 | 209 | 205 | 203 | 202 | 200 | 197 | 196 | 195 | 201 | 205 | 209 | 211 | 215 | 217 | 219 | 220 | 221 | 220 | 220 | 220 | 218 | 218 | 217 | 210 | 234 | 214 | 136 | 127 | 212 | 208 | 206 | 204 | 202 | 199 | 197 | 196 | 203 | 207 | 211 | 215 | 217 | 218 | 221 | 222 | 223 | 223 | 223 | 223 | 222 | 220 | 219 | 215 | 245 | 211 | 138 | 127 | 212 | 209 | 208 | 206 | 204 | 201 | 200 | 197 | 205 | 209 | 213 | 216 | 218 | 221 | 223 | 225 | 225 | 226 | 225 | 225 | 224 | 221 | 222 | 211 | 196 | 171 | 117 | 125 | 218 | 209 | 209 | 208 | 205 | 202 | 199 | 197 | 206 | 211 | 215 | 218 | 221 | 224 | 226 | 227 | 228 | 228 | 228 | 227 | 225 | 228 | 248 | 205 | 135 | 103 | 83 | 146 | 224 | 210 | 211 | 210 | 206 | 204 | 201 | 199 | 208 | 213 | 217 | 220 | 224 | 226 | 227 | 229 | 230 | 230 | 229 | 226 | 224 | 206 | 203 | 208 | 162 | 122 | 67 | 188 | 226 | 212 | 214 | 212 | 209 | 206 | 203 | 201 | 210 | 214 | 218 | 222 | 225 | 227 | 230 | 230 | 232 | 232 | 226 | 241 | 241 | 173 | 112 | 174 | 233 | 151 | 109 | 168 | 210 | 221 | 216 | 213 | 210 | 208 | 205 | 203 | 210 | 216 | 219 | 223 | 226 | 229 | 231 | 233 | 228 | 236 | 199 | 159 | 215 | 180 | 130 | 150 | 240 | 144 | 123 | 104 | 137 | 161 | 215 | 215 | 211 | 210 | 208 | 205 | 211 | 217 | 221 | 224 | 228 | 231 | 232 | 232 | 247 | 248 | 200 | 135 | 166 | 204 | 149 | 127 | 202 | 154 | 142 | 123 | 154 | 116 | 187 | 223 | 211 | 211 | 209 | 207 | 213 | 218 | 222 | 226 | 229 | 232 | 232 | 240 | 251 | 234 | 220 | 177 | 135 | 198 | 148 | 114 | 166 | 166 | 154 | 134 | 149 | 103 | 177 | 226 | 213 | 213 | 210 | 208 | 212 | 218 | 223 | 226 | 230 | 233 | 232 | 255 | 250 | 221 | 229 | 182 | 128 | 167 | 147 | 113 | 156 | 127 | 118 | 148 | 127 | 93 | 174 | 229 | 214 | 215 | 212 | 210 | 212 | 217 | 223 | 226 | 230 | 232 | 232 | 249 | 242 | 194 | 200 | 180 | 120 | 134 | 144 | 96 | 185 | 187 | 100 | 170 | 156 | 95 | 150 | 230 | 214 | 213 | 211 | 209 | 215 | 219 | 224 | 229 | 231 | 234 | 236 | 238 | 238 | 212 | 199 | 159 | 99 | 128 | 128 | 77 | 180 | 194 | 104 | 152 | 162 | 92 | 130 | 230 | 215 | 214 | 210 | 207 | 215 | 220 | 225 | 228 | 231 | 234 | 236 | 242 | 251 | 233 | 211 | 148 | 123 | 132 | 141 | 105 | 164 | 168 | 143 | 145 | 133 | 87 | 174 | 227 | 213 | 214 | 215 | 218 | 214 | 220 | 226 | 229 | 231 | 234 | 235 | 240 | 255 | 240 | 226 | 153 | 131 | 131 | 155 | 168 | 220 | 197 | 168 | 147 | 97 | 115 | 229 | 227 | 228 | 225 | 204 | 161 | 217 | 223 | 229 | 233 | 235 | 238 | 241 | 243 | 255 | 249 | 221 | 169 | 150 | 139 | 203 | 232 | 209 | 176 | 139 | 131 | 93 | 182 | 234 | 193 | 166 | 138 | 92 | 66 | 165 | 165 | 167 | 169 | 170 | 170 | 167 | 167 | 238 | 245 | 212 | 191 | 163 | 154 | 214 | 223 | 188 | 155 | 131 | 118 | 95 | 124 | 103 | 65 | 50 | 47 | 54 | 59 | 139 | 141 | 140 | 141 | 140 | 137 | 140 | 124 | 201 | 244 | 213 | 195 | 177 | 145 | 180 | 191 | 162 | 136 | 120 | 100 | 70 | 49 | 47 | 50 | 57 | 54 | 47 | 50 | 147 | 148 | 147 | 147 | 147 | 148 | 158 | 126 | 124 | 242 | 215 | 194 | 176 | 136 | 137 | 135 | 129 | 115 | 100 | 70 | 53 | 56 | 57 | 49 | 48 | 58 | 53 | 46 | 148 | 148 | 146 | 146 | 146 | 146 | 148 | 121 | 64 | 148 | 207 | 197 | 154 | 117 | 119 | 116 | 119 | 91 | 54 | 69 | 46 | 43 | 53 | 52 | 44 | 48 | 56 | 55 | 148 | 150 | 151 | 149 | 148 | 149 | 143 | 95 | 79 | 74 | 115 | 144 | 125 | 120 | 112 | 105 | 93 | 52 | 24 | 53 | 63 | 33 | 41 | 51 | 48 | 45 | 49 | 55 | 149 | 150 | 150 | 148 | 147 | 151 | 124 | 82 | 84 | 81 | 69 | 81 | 111 | 103 | 84 | 75 | 53 | 28 | 26 | 40 | 64 | 48 | 29 | 46 | 49 | 46 | 46 | 53 |
# Standardize the class column to the name of targetVar if required
Xy_test = Xy_test.rename(columns={'label': 'targetVar'})
X_test_df = Xy_test.iloc[:,1:totCol]
y_test_df = Xy_test.iloc[:,0]
print("Xy_test.shape: {} X_test_df.shape: {} y_test_df.shape: {}".format(Xy_test.shape, X_test_df.shape, y_test_df.shape))
Xy_test.shape: (7172, 785) X_test_df.shape: (7172, 784) y_test_df.shape: (7172,)
if notifyStatus: status_notify('(TensorFlow Multi-Class) Task 1 - Prepare Environment completed on ' + datetime.now().strftime('%A %B %d, %Y %I:%M:%S %p'))
if notifyStatus: status_notify('(TensorFlow Multi-Class) Task 2 - Summarize and Visualize Data has begun on ' + datetime.now().strftime('%A %B %d, %Y %I:%M:%S %p'))
# Set up the number of row and columns for visualization display. dispRow * dispCol should be >= totAttr
dispCol = 4
if totAttr % dispCol == 0 :
dispRow = totAttr // dispCol
else :
dispRow = (totAttr // dispCol) + 1
# Set figure width to display the data visualization plots
fig_size = plt.rcParams["figure.figsize"]
fig_size[0] = dispCol*4
fig_size[1] = dispRow*4
plt.rcParams["figure.figsize"] = fig_size
# Histograms for each attribute
X_train_df.hist(layout=(dispRow,dispCol))
plt.show()
# Box and Whisker plot for each attribute
X_train_df.plot(kind='box', subplots=True, layout=(dispRow,dispCol))
plt.show()
# Correlation matrix
fig = plt.figure(figsize=(16,12))
ax = fig.add_subplot(111)
correlations = X_train_df.corr(method='pearson')
cax = ax.matshow(correlations, vmin=-1, vmax=1)
fig.colorbar(cax)
plt.show()
if notifyStatus: status_notify('(TensorFlow Multi-Class) Task 2 - Summarize and Visualize Data completed on ' + datetime.now().strftime('%A %B %d, %Y %I:%M:%S %p'))
if notifyStatus: status_notify('(TensorFlow Multi-Class) Task 3 - Pre-process Data has begun on ' + datetime.now().strftime('%A %B %d, %Y %I:%M:%S %p'))
# Compose pipeline for the numerical and categorical features
numeric_columns = X_train_df.select_dtypes(include=['int','float']).columns
numeric_transformer = Pipeline(steps=[
('imputer', SimpleImputer(strategy='constant', fill_value=0)),
('scaler', preprocessing.StandardScaler())
])
categorical_columns = X_train_df.select_dtypes(include=['object','category']).columns
categorical_transformer = Pipeline(steps=[
('imputer', SimpleImputer(strategy='constant', fill_value='NA')),
('onehot', preprocessing.OneHotEncoder(sparse=False, handle_unknown='ignore'))
])
print("Number of numerical columns:", len(numeric_columns))
print("Number of categorical columns:", len(categorical_columns))
print("Total number of columns in the dataframe:", X_train_df.shape[1])
Number of numerical columns: 784 Number of categorical columns: 0 Total number of columns in the dataframe: 784
preprocessor = ColumnTransformer(transformers=[
('num', numeric_transformer, numeric_columns),
# ('cat', categorical_transformer, categorical_columns)
])
X_train = preprocessor.fit_transform(X_train_df)
print("Transformed X_train.shape:", X_train.shape)
Transformed X_train.shape: (27455, 784)
# Apply feature scaling and transformation to the test dataset
X_test = preprocessor.transform(X_test_df)
print("Transformed X_test.shape:", X_test.shape)
Transformed X_test.shape: (7172, 784)
# Not applicable for this iteration of the project
# Finalize the training dataset for the modeling activities
class_encoder = preprocessing.LabelEncoder()
y_train = tf.keras.utils.to_categorical(class_encoder.fit_transform(y_train_df))
print("X_train.shape: {} y_train.shape: {}".format(X_train.shape, y_train.shape))
X_train.shape: (27455, 784) y_train.shape: (27455, 24)
# Finalize the test dataset for the modeling activities
y_test = tf.keras.utils.to_categorical(class_encoder.transform(y_test_df))
print("X_test.shape: {} y_test.shape: {}".format(X_test.shape, y_test.shape))
X_test.shape: (7172, 784) y_test.shape: (7172, 24)
if notifyStatus: status_notify('(TensorFlow Multi-Class) Task 3 - Pre-process Data completed on ' + datetime.now().strftime('%A %B %d, %Y %I:%M:%S %p'))
if notifyStatus: status_notify('(TensorFlow Multi-Class) Task 4 - Train and Evaluate Models has begun on ' + datetime.now().strftime('%A %B %d, %Y %I:%M:%S %p'))
# Define the function for plotting training results for comparison
def plot_metrics(history):
fig, axs = plt.subplots(1, 2, figsize=(24, 15))
metrics = [train_loss, train_metric]
for n, metric in enumerate(metrics):
name = metric.replace("_"," ").capitalize()
plt.subplot(2,2,n+1)
plt.plot(history.epoch, history.history[metric], color='blue', label='Train')
plt.plot(history.epoch, history.history['val_'+metric], color='red', linestyle="--", label='Val')
plt.xlabel('Epoch')
plt.ylabel(name)
# if metric == train_loss:
# plt.ylim([0, plt.ylim()[1]])
# else:
# plt.ylim([0.5, 1.1])
plt.legend()
# Define the default numbers of input/output for modeling
num_inputs = X_train.shape[1]
# number of target classes for multi-class modeling
num_outputs = 24
# Define the baseline model for benchmarking
def create_nn_model(n_inputs=num_inputs, n_outputs=num_outputs, layer1_nodes=256, layer2_nodes=128, layer3_nodes=128, layer4_nodes=64, layer5_nodes=64,
layer1_dropout=0.25, layer2_dropout=0.25, layer3_dropout=0.25, layer4_dropout=0.25, layer5_dropout=0.5,
opt_param=default_optimizer, init_param=default_kernel_init):
nn_model = keras.Sequential([
keras.layers.Dense(layer1_nodes, input_shape=(n_inputs,), activation='relu', kernel_initializer=init_param),
keras.layers.Dropout(layer1_dropout),
keras.layers.BatchNormalization(),
keras.layers.Dense(layer2_nodes, activation='relu', kernel_initializer=init_param),
keras.layers.Dropout(layer2_dropout),
keras.layers.BatchNormalization(),
keras.layers.Dense(layer3_nodes, activation='relu', kernel_initializer=init_param),
keras.layers.Dropout(layer3_dropout),
keras.layers.BatchNormalization(),
keras.layers.Dense(layer4_nodes, activation='relu', kernel_initializer=init_param),
keras.layers.Dropout(layer4_dropout),
keras.layers.BatchNormalization(),
keras.layers.Dense(layer5_nodes, activation='relu', kernel_initializer=init_param),
keras.layers.Dropout(layer5_dropout),
keras.layers.BatchNormalization(),
keras.layers.Dense(n_outputs, activation='softmax', kernel_initializer=init_param)
])
nn_model.compile(loss=default_loss, optimizer=opt_param, metrics=default_metrics)
return nn_model
# Initialize the default model and get a baseline result
startTimeModule = datetime.now()
results = list()
iteration = 0
reset_random(seedNum)
final_model = create_nn_model()
nn_model_history = final_model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=default_epoch, batch_size=default_batch, verbose=1)
print('Total time for model fitting and cross validating:', (datetime.now() - startTimeModule))
Epoch 1/50 858/858 [==============================] - 6s 7ms/step - loss: 3.5323 - accuracy: 0.0644 - val_loss: 2.7429 - val_accuracy: 0.2295 Epoch 2/50 858/858 [==============================] - 6s 7ms/step - loss: 3.0846 - accuracy: 0.1254 - val_loss: 2.3555 - val_accuracy: 0.3182 Epoch 3/50 858/858 [==============================] - 6s 7ms/step - loss: 2.7340 - accuracy: 0.1879 - val_loss: 2.0565 - val_accuracy: 0.4221 Epoch 4/50 858/858 [==============================] - 6s 7ms/step - loss: 2.4141 - accuracy: 0.2590 - val_loss: 1.8139 - val_accuracy: 0.4870 Epoch 5/50 858/858 [==============================] - 6s 7ms/step - loss: 2.1650 - accuracy: 0.3194 - val_loss: 1.6265 - val_accuracy: 0.5130 Epoch 6/50 858/858 [==============================] - 6s 6ms/step - loss: 1.9424 - accuracy: 0.3793 - val_loss: 1.4506 - val_accuracy: 0.5715 Epoch 7/50 858/858 [==============================] - 6s 6ms/step - loss: 1.7503 - accuracy: 0.4367 - val_loss: 1.2920 - val_accuracy: 0.6196 Epoch 8/50 858/858 [==============================] - 6s 7ms/step - loss: 1.5761 - accuracy: 0.4901 - val_loss: 1.1655 - val_accuracy: 0.6602 Epoch 9/50 858/858 [==============================] - 6s 7ms/step - loss: 1.4147 - accuracy: 0.5395 - val_loss: 1.0554 - val_accuracy: 0.6613 Epoch 10/50 858/858 [==============================] - 6s 7ms/step - loss: 1.2827 - accuracy: 0.5871 - val_loss: 1.0098 - val_accuracy: 0.6619 Epoch 11/50 858/858 [==============================] - 5s 6ms/step - loss: 1.1537 - accuracy: 0.6243 - val_loss: 0.9243 - val_accuracy: 0.6861 Epoch 12/50 858/858 [==============================] - 6s 6ms/step - loss: 1.0524 - accuracy: 0.6550 - val_loss: 0.9070 - val_accuracy: 0.6857 Epoch 13/50 858/858 [==============================] - 6s 7ms/step - loss: 0.9760 - accuracy: 0.6800 - val_loss: 0.8879 - val_accuracy: 0.6857 Epoch 14/50 858/858 [==============================] - 6s 7ms/step - loss: 0.8878 - accuracy: 0.7087 - val_loss: 0.8669 - val_accuracy: 0.7047 Epoch 15/50 858/858 [==============================] - 5s 6ms/step - loss: 0.8214 - accuracy: 0.7273 - val_loss: 0.8030 - val_accuracy: 0.7093 Epoch 16/50 858/858 [==============================] - 6s 6ms/step - loss: 0.7424 - accuracy: 0.7551 - val_loss: 0.8406 - val_accuracy: 0.7128 Epoch 17/50 858/858 [==============================] - 6s 6ms/step - loss: 0.7047 - accuracy: 0.7654 - val_loss: 0.8442 - val_accuracy: 0.7132 Epoch 18/50 858/858 [==============================] - 5s 6ms/step - loss: 0.6403 - accuracy: 0.7839 - val_loss: 0.8343 - val_accuracy: 0.7234 Epoch 19/50 858/858 [==============================] - 6s 7ms/step - loss: 0.5971 - accuracy: 0.8021 - val_loss: 0.8392 - val_accuracy: 0.7250 Epoch 20/50 858/858 [==============================] - 6s 7ms/step - loss: 0.5700 - accuracy: 0.8080 - val_loss: 0.8813 - val_accuracy: 0.7193 Epoch 21/50 858/858 [==============================] - 5s 6ms/step - loss: 0.5332 - accuracy: 0.8220 - val_loss: 0.8691 - val_accuracy: 0.7220 Epoch 22/50 858/858 [==============================] - 6s 7ms/step - loss: 0.5108 - accuracy: 0.8263 - val_loss: 0.9057 - val_accuracy: 0.7217 Epoch 23/50 858/858 [==============================] - 6s 7ms/step - loss: 0.4773 - accuracy: 0.8406 - val_loss: 0.8623 - val_accuracy: 0.7333 Epoch 24/50 858/858 [==============================] - 6s 7ms/step - loss: 0.4604 - accuracy: 0.8468 - val_loss: 0.9004 - val_accuracy: 0.7278 Epoch 25/50 858/858 [==============================] - 6s 7ms/step - loss: 0.4269 - accuracy: 0.8575 - val_loss: 0.8835 - val_accuracy: 0.7368 Epoch 26/50 858/858 [==============================] - 5s 6ms/step - loss: 0.4119 - accuracy: 0.8633 - val_loss: 0.9203 - val_accuracy: 0.7308 Epoch 27/50 858/858 [==============================] - 6s 7ms/step - loss: 0.4073 - accuracy: 0.8652 - val_loss: 0.9358 - val_accuracy: 0.7253 Epoch 28/50 858/858 [==============================] - 6s 7ms/step - loss: 0.3779 - accuracy: 0.8772 - val_loss: 0.9707 - val_accuracy: 0.7377 Epoch 29/50 858/858 [==============================] - 6s 7ms/step - loss: 0.3720 - accuracy: 0.8797 - val_loss: 0.9794 - val_accuracy: 0.7351 Epoch 30/50 858/858 [==============================] - 6s 7ms/step - loss: 0.3439 - accuracy: 0.8896 - val_loss: 0.9411 - val_accuracy: 0.7521 Epoch 31/50 858/858 [==============================] - 6s 7ms/step - loss: 0.3365 - accuracy: 0.8930 - val_loss: 0.9908 - val_accuracy: 0.7529 Epoch 32/50 858/858 [==============================] - 6s 7ms/step - loss: 0.3379 - accuracy: 0.8929 - val_loss: 1.0078 - val_accuracy: 0.7455 Epoch 33/50 858/858 [==============================] - 6s 7ms/step - loss: 0.3025 - accuracy: 0.9072 - val_loss: 1.0196 - val_accuracy: 0.7457 Epoch 34/50 858/858 [==============================] - 6s 7ms/step - loss: 0.2939 - accuracy: 0.9120 - val_loss: 1.0528 - val_accuracy: 0.7494 Epoch 35/50 858/858 [==============================] - 5s 6ms/step - loss: 0.2936 - accuracy: 0.9103 - val_loss: 1.0357 - val_accuracy: 0.7535 Epoch 36/50 858/858 [==============================] - 5s 6ms/step - loss: 0.2792 - accuracy: 0.9175 - val_loss: 1.0467 - val_accuracy: 0.7538 Epoch 37/50 858/858 [==============================] - 6s 7ms/step - loss: 0.2700 - accuracy: 0.9205 - val_loss: 1.0724 - val_accuracy: 0.7522 Epoch 38/50 858/858 [==============================] - 6s 7ms/step - loss: 0.2605 - accuracy: 0.9244 - val_loss: 1.0465 - val_accuracy: 0.7602 Epoch 39/50 858/858 [==============================] - 6s 7ms/step - loss: 0.2558 - accuracy: 0.9242 - val_loss: 1.0413 - val_accuracy: 0.7627 Epoch 40/50 858/858 [==============================] - 6s 7ms/step - loss: 0.2341 - accuracy: 0.9319 - val_loss: 1.0052 - val_accuracy: 0.7810 Epoch 41/50 858/858 [==============================] - 6s 6ms/step - loss: 0.2382 - accuracy: 0.9332 - val_loss: 1.0054 - val_accuracy: 0.7736 Epoch 42/50 858/858 [==============================] - 6s 7ms/step - loss: 0.2211 - accuracy: 0.9377 - val_loss: 1.0945 - val_accuracy: 0.7586 Epoch 43/50 858/858 [==============================] - 5s 6ms/step - loss: 0.2146 - accuracy: 0.9401 - val_loss: 1.0469 - val_accuracy: 0.7692 Epoch 44/50 858/858 [==============================] - 6s 7ms/step - loss: 0.2126 - accuracy: 0.9405 - val_loss: 1.0682 - val_accuracy: 0.7790 Epoch 45/50 858/858 [==============================] - 6s 7ms/step - loss: 0.2123 - accuracy: 0.9420 - val_loss: 1.0883 - val_accuracy: 0.7708 Epoch 46/50 858/858 [==============================] - 6s 7ms/step - loss: 0.1976 - accuracy: 0.9459 - val_loss: 1.1273 - val_accuracy: 0.7581 Epoch 47/50 858/858 [==============================] - 6s 7ms/step - loss: 0.2082 - accuracy: 0.9427 - val_loss: 1.1329 - val_accuracy: 0.7595 Epoch 48/50 858/858 [==============================] - 6s 6ms/step - loss: 0.1973 - accuracy: 0.9460 - val_loss: 1.0786 - val_accuracy: 0.7716 Epoch 49/50 858/858 [==============================] - 6s 7ms/step - loss: 0.1924 - accuracy: 0.9488 - val_loss: 1.1085 - val_accuracy: 0.7653 Epoch 50/50 858/858 [==============================] - 6s 7ms/step - loss: 0.1863 - accuracy: 0.9493 - val_loss: 1.0900 - val_accuracy: 0.7793 Total time for model fitting and cross validating: 0:04:54.323254
plot_metrics(nn_model_history)
# Summarize the final model
final_model.summary()
Model: "sequential" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense (Dense) (None, 256) 200960 _________________________________________________________________ dropout (Dropout) (None, 256) 0 _________________________________________________________________ batch_normalization (BatchNo (None, 256) 1024 _________________________________________________________________ dense_1 (Dense) (None, 128) 32896 _________________________________________________________________ dropout_1 (Dropout) (None, 128) 0 _________________________________________________________________ batch_normalization_1 (Batch (None, 128) 512 _________________________________________________________________ dense_2 (Dense) (None, 128) 16512 _________________________________________________________________ dropout_2 (Dropout) (None, 128) 0 _________________________________________________________________ batch_normalization_2 (Batch (None, 128) 512 _________________________________________________________________ dense_3 (Dense) (None, 64) 8256 _________________________________________________________________ dropout_3 (Dropout) (None, 64) 0 _________________________________________________________________ batch_normalization_3 (Batch (None, 64) 256 _________________________________________________________________ dense_4 (Dense) (None, 64) 4160 _________________________________________________________________ dropout_4 (Dropout) (None, 64) 0 _________________________________________________________________ batch_normalization_4 (Batch (None, 64) 256 _________________________________________________________________ dense_5 (Dense) (None, 24) 1560 ================================================================= Total params: 266,904 Trainable params: 265,624 Non-trainable params: 1,280 _________________________________________________________________
if notifyStatus: status_notify('(TensorFlow Multi-Class) Task 4 - Train and Evaluate Models completed on ' + datetime.now().strftime('%A %B %d, %Y %I:%M:%S %p'))
if notifyStatus: status_notify('(TensorFlow Multi-Class) Task 5 - Finalize Model and Present Analysis has begun on ' + datetime.now().strftime('%A %B %d, %Y %I:%M:%S %p'))
# Not applicable for this iteration of modeling
if notifyStatus: status_notify('(TensorFlow Multi-Class) Task 5 - Finalize Model and Present Analysis completed on ' + datetime.now().strftime('%A %B %d, %Y %I:%M:%S %p'))
print ('Total time for the script:',(datetime.now() - startTimeScript))
Total time for the script: 0:08:21.144189